effect of real effective exchange rate volatility on...
TRANSCRIPT
Effect of Real Effective Exchange Rate Volatility on Foreign Direct
Investment in South Africa
A Research Report
presented to
The Graduate School of Business
University of Cape Town
in partial fulfilment
of the requirements for the
Masters of Business Administration Degree
by
Ronald de la Cruz
December 2008
Supervisor: Barry Standish
i
DECLARATION
This report is not confidential. It may be freely used by the Graduate School of Business.
I certify that the report is my own work and all information sources have been accurately
referenced.
Signed:
______________________________
Ronald de la Cruz
ii
ACKNOWLEDGEMENTS
First and foremost, thanks and praise to my Lord Jesus Christ for giving me the daily strength
and ability to complete (arguably) the most demanding period in my life.
To my supervisor, Barry Standish, for the constructive feedback throughout the research
process.
To Madalet Sessions, without whose assistance I‟d still be reading up on econometric
analysis and techniques!
To my family, for your unconditional understanding and continuous support.
And last, but by no means least, to Shauna and Daniel. Without your support, encouragement
and understanding, the entire MBA journey would have been impossible.
iii
ABSTRACT
Purpose – This study investigates the impact of real effective exchange rate (REER)
volatility on foreign direct investment (FDI) to South Africa during the period 1980 to 2006.
Design/methodology/approach – Time series data covering the period 1980 to 2006 is used.
An autoregressive conditional heteroskedastic (ARCH) model is employed for the
determination of REER volatility. The Engle-Granger two-step model, utilizing cointegration
analysis and error correction modelling, is used to determine the long-run and short-run
(respectively) relationships between the explanatory variables.
Findings – The findings show that REER volatility has impacted negatively on FDI during
the period under consideration. The most notable finding is that the REER level does not play
a role in the decision to invest. The amount of FDI already present is found to play a
significant role in the decision for further FDI. It is also found that openness of the economy
and political stability in South Africa does not play a significant role in the FDI decision.
Originality/value – No other similar studies for South Africa has been found. This study
therefore provides a basis for further research in this particular area.
Keywords – Real Effective Exchange Rate; Volatility; Foreign Direct Investment;
Autoregressive Conditional Heteroskedasticity; Cointegration; Unit Root; Error Correction
Model.
CONTENTS
DECLARATION ........................................................................................................................I
ACKNOWLEDGEMENTS ...................................................................................................... II
ABSTRACT ............................................................................................................................. III
1. INTRODUCTION ............................................................................................................. 1
2. AREA OF STUDY ............................................................................................................ 2
2.1. Background ................................................................................................................ 2
2.2. Motivation for Research ............................................................................................ 3
2.3. Research Questions .................................................................................................... 4
2.4. Research Hypothesis .................................................................................................. 4
3. LITERATURE REVIEW .................................................................................................. 5
3.1. Foreign Direct Investment: Importance and Implications ......................................... 5
3.2. Uncertainty and Foreign Direct Investment ............................................................... 8
3.3. Uncertainty and the Real Exchange Rate................................................................. 12
4. THEORETICAL BACKGROUND ................................................................................. 13
4.1. Purchasing Power Parity and the Real Exchange Rate ............................................ 13
4.2. The South African Real Exchange Rate .................................................................. 15
5. METHODOLOGY .......................................................................................................... 18
5.1. Develop Basic Estimable Model .............................................................................. 19
5.2. Data Collection ........................................................................................................ 20
5.3. Description of Variables .......................................................................................... 21
5.4. Volatility Measure ................................................................................................... 24
5.5. Estimation Procedure ............................................................................................... 26
5.5.1. Estimating the Volatility Index ........................................................................ 26
5.5.2. Unit Root Tests ................................................................................................ 26
5.5.3. Error Correction Model.................................................................................... 27
5.6. Engle-Granger Two-Step Model.............................................................................. 28
6. FINDINGS ....................................................................................................................... 30
6.1. Exchange Rate Volatility ......................................................................................... 31
6.2. Unit Root and Cointegration Tests .......................................................................... 32
6.3. Engle-Granger Step One: Long-Run Relationship Model ....................................... 37
6.3.1. Long-Run Model 1 ........................................................................................... 37
6.3.2. Long-Run Model 2 ........................................................................................... 38
6.4. Engle-Granger Step Two: Error Correction Model ................................................. 40
6.4.1. ECM Model 1 Results ...................................................................................... 41
6.4.2. ECM Model 2 Results ...................................................................................... 41
7. DISCUSSION OF FINDINGS ........................................................................................ 43
8. CONCLUSION ................................................................................................................ 45
9. BIBLIOGRAPHY ............................................................................................................ 46
9.1. Journals .................................................................................................................... 46
9.2. Web Sites ................................................................................................................. 49
9.3. Books ....................................................................................................................... 49
APPENDIX 1: UNIT ROOT TEST OUTPUTS - LEVELS ................................................... 51
APPENDIX 2: UNIT ROOT TESTS – FIRST DIFFERENCE .............................................. 53
APPENDIX 3: MODEL 1 - OLS REGRESSION STEP-WISE REDUCTION (LPGDP)
INCLUDED ............................................................................................................................. 55
APPENDIX 4: MODEL 2 - OLS REGRESSION STEP-WISE REDUCTION (LPGDP)
EXCLUDED ............................................................................................................................ 58
TABLE OF FIGURES
Figure 1: FDI Flows to Southern Africa .................................................................................... 6
Figure 2: South African Nominal and Real Effective Exchange Rates - 1970 to 2006........... 17
Figure 3: Methodology Sequence ............................................................................................ 18
Figure 4: South African total FDI 1980Q1 - 2006Q4 .............................................................. 21
Figure 5: South African REER 1980Q1 - 2006Q4 .................................................................. 22
Figure 6: South African REER volatility 1980Q1 - 2006Q4 ................................................... 22
Figure 7: South African Economy Openness 1980Q1 - 2006Q4 ............................................ 23
Figure 8: South African per capita GDP 1980Q1 - 2006Q4 ................................................... 23
Figure 9: South African FDI stock 1980Q1 - 2006Q4 ............................................................ 24
Figure 10: ADF unit root test graphical output ........................................................................ 34
Figure 11: Long-Run Model 1 Actual vs Predicted ................................................................. 37
Figure 12: Long-Run Model 2 Actual vs Predicted ................................................................. 38
LIST OF TABLES
Table 1: FDI Motivators ............................................................................................................ 7
Table 2: Major Regression Results of Previous Empirical Studies ......................................... 11
Table 3: Regime changes in the South African foreign exchange market ............................... 16
Table 4: Variables under consideration ................................................................................... 21
Table 5: Estimation of REER volatility ................................................................................... 31
Table 6: ADF unit root test results ........................................................................................... 33
Table 7: Model 1 cointegration test (including LPGDP)......................................................... 35
Table 8: Model 2 cointegration test (excluding LPGDP) ........................................................ 35
Table 9: ECM results for Model 1 ........................................................................................... 41
Table 10: ECM results for Model 2 ......................................................................................... 41
1
1. INTRODUCTION
According to Bénassy-Guéré et al (2007), there has been a growing interest in the
determinants of foreign direct investment (FDI) in developing countries. FDI is considered as
one of the most stable components of capital flows to such countries, as it can also be a
vehicle for technological progress through the use and dissemination of improved production
techniques.
This study evaluates the effect of real effective exchange rate (REER) volatility as a
determinant of FDI to South Africa, during the period 1980 to 2006. The methodology is
based on similar studies performed by Cushman (1988), Xing and Wan (2006), and
Kyereboah-Coleman and Agyire-Tettey (2008). The research is a correlation study, which
examines the extent to which differences in one characteristic or variable are related to
differences in one or more other characteristics or variables.
Following a brief background to the issue and the motivation for the research, Section 3
provides a review of the literature related to this topic. The review considers the importance
and implications of FDI, which includes a synopsis of the different types of FDI as well as
some of the motivators behind FDI. Section 3 concludes by reviewing some of the literature
related to uncertainty, as it relates to FDI as well as the REER, and the implications of
uncertainty on both these factors.
Section 4 documents the theoretical background with respect to the real exchange rate (RER).
The difference between the RER and the REER is clarified, and the relationship between
purchasing power parity and the RER is addressed. A brief history of the South African
exchange rate system is presented, with the section concluding with a brief description of the
volatility experienced by the South African REER from the 1970‟s to the present.
Section 5 describes the methodology followed for the analysis of economic data. This section
describes the econometric and statistical methods employed to address the research questions
posed. These methods include unit root testing for stationarity in the explanatory variables,
evaluating exchange rate volatility using the autoregressive conditional heteroskedastic
methodology, and cointegration tests to ensure that spurious regression results are not
obtained. The results of these tests are ultimately combined in an ordinary least squares
2
regression, which forms the basis of the Engle-Granger two-step analysis of the long and
short-run determinants of FDI to South Africa.
Having completed the econometric and statistical analysis, Section 6 discusses the findings in
greater detail. The key findings are that the REER volatility has indeed negatively impacted
on FDI to South Africa during the period in question, and that the level of the REER does not
play a role in the FDI decision.
Section 7 provides greater clarity on the discussion in Section 6; most notably the finding that
the REER level does not play a role in the FDI decision. The argument is made that FDI is a
long-term decision, and that exchange rate fluctuation brought about by volatility could
potentially negatively affect future profits related to FDI – especially fixed capital FDI. With
significant exchange rate volatility, the current exchange rate level is not an accurate or
dependable measure of future exchange rates, and would therefore not be considered in the
decision to invest for the long term.
Section 8 concludes the report, providing a synopsis of the salient points established by the
research.
2. AREA OF STUDY
This section looks at some of the reasons for this study, starting with a brief background of
FDI to Southern Africa. The amounts as well as receiving countries are identified, followed
by a synopsis of similar past studies. The section concludes with the hypothesis being
addressed by this research.
2.1. BACKGROUND
According to the United Nations Conference on Trade and Development (UNCTAD, 2007),
in 2005, South Africa received a net amount of US$ 6,311 million in FDI, representing
approximately 90% of the total net FDI in Southern Africa.
These FDI flows have occurred despite significant volatility in the South African exchange
rate; particularly the REER. As will be demonstrated in the succeeding sections of this report,
3
exchange rate volatility has been found to have a negative effect on FDI flows to receiving
countries.
The purpose of this research is to determine the effect of exchange rate volatility, particularly
REER volatility, on FDI in South Africa – the period under consideration being 1980 to
2006. This specific period is chosen based on the fact that it effectively encompasses the
following three distinct periods in South Africa‟s history:
- 1980 to 1990: non-democratic whites-only rule;
- 1990 to 1994: transition to a democratically elected government representing the
majority of the population;
- 1994 to 2006: democratically elected government.
Given the relatively volatile South African REER (Takaendesa et al, 2006:80; Mtonga,
2006), and the importance of FDI to developing economies (Kandiero and Chitiga,
2006:355), the research will contribute to broadening the understanding of the impacts and
effect of a volatile REER on FDI flows to South Africa.
2.2. MOTIVATION FOR RESEARCH
Froot and Stein (1991), Klein and Rosengren (1994), Bayoumi and Lipworth (1998),
Goldberg and Klein (1998), Ito (2000), Sazanami and Wong (1997), and Sazanami,
Yoshimura and Kiyota (2003) have all examined the effects of exchange rates on FDI (Kiyota
and Urata, 2004:1502).
From an African perspective, similar studies have also been conducted for Ghana
(Kyereboah-Coleman & Agyire-Tettey, 2008) and Lesotho (Malefane, 2007). Although an
abundance of literature related to the determinants of the REER in South Africa exists,
similar studies related to REER volatility for South Africa could not be found. The research
therefore attempts to address the apparent lack of research in this area.
4
2.3. RESEARCH QUESTIONS
The study will use both theoretical and empirical literature and data to address the following
question:
What effect has real effective exchange rate volatility had on FDI in South Africa
during the past twenty years?
2.4. RESEARCH HYPOTHESIS
The research tests the following hypothesis:
Volatility in the South African real effective exchange rate has impacted negatively
on FDI during the past twenty years.
5
3. LITERATURE REVIEW
Given the relative lack of South African specific research on the topic, this section aims to
provide a detailed review of past studies of a similar nature. It is hoped that by comparing
similar studies of different countries, a clearer understanding of the issues contributing to FDI
flows as well REER volatility can be obtained. In addition, it is expected that the literature
review will assist in the identification of explanatory variables used in previous studies,
which may be relevant to the South African case.
The section starts with a review of literature related to the importance of FDI, in order to
provide an appreciation for the need for FDI. Thereafter, the effect of uncertainty on FDI
flows is considered. The section concludes with a review of the literature related to
uncertainty associated with REER volatility.
3.1. FOREIGN DIRECT INVESTMENT: IMPORTANCE AND IMPLICATIONS
The UNCTAD defines FDI as
“…an investment involving a long-term relationship and reflecting a lasting
interest in and control by a resident entity in one economy (foreign direct
investor or parent enterprise) of an enterprise resident in a different
economy (FDI enterprise or affiliate enterprise or foreign affiliate). Such
investment involves both the initial transaction between the two entities and
all subsequent transactions between them and among foreign affiliates.”
(UNCTAD, 2007:ix)
According to Lemi and Asefa (2003), capital flows to developing countries have grown
significantly as world economies become more integrated. These inflows stimulate capital
accumulation, by adding to domestic savings and raising the recipient economy‟s efficiency.
These efficiency improvements are realised through improving resource allocation,
provoking competition, improving human capital, deepening domestic financial markets, and
reducing local capital costs (Kandiero and Chitiga, 2006:355). Figure 1 below depicts FDI
flows to Southern African countries. As can be seen, South Africa receives the bulk of FDI
flows into this region.
6
FDI Flows to Southern Africa
110
89 55
-349
360
212 43
0
289
4 16 31 28 27 42 53 470 29 185 378
187
159
248
361
-765
-105
617
9969
1156
169
-553
6311
17 25 74 69 90
-50
59
-35
-2000
0
2000
4000
6000
8000
10000
12000
1980 1990 2000 2001 2002 2003 2004 2005
Year
Millio
n o
f U
S$
Botswana Lesotho Namibia South Africa Swaziland Adapted from UNCTAD (2007)
Figure 1: FDI Flows to Southern Africa
The most notable observation from Figure 1 is the significant spikes in FDI during 2001 and
2005. These increases are attributable to the unbundling and de-listing of De Beers in South
Africa, and the purchase of a controlling stake in ABSA by Barclays plc respectively. In the
De Beers case, the transaction was recorded as an FDI inflow because Anglo-American
purchased De Beers shares by paying the mainly South African-based owners in Anglo-
American shares (UNCTAD, 2002).
Given the benefits of FDI to the receiving country, it is important for developing countries to
provide an environment that is not only amenable to, but also promotes FDI. In this respect,
though, the type of FDI that is sought is not the unsustainable FDI witnessed by the once-off
spikes caused by purchases or sales of large companies. In such an event, although recorded
as FDI inflows, the bulk of the benefit is not experienced by the country and its inhabitants,
but rather by a relatively small group of shareholders.
7
Kandiero and Chitiga (2006:356) identified several reasons why foreign firms choose to
invest away from their home countries. Based on earlier work by Dunning (1993), the
principal factors motivating FDI from most industrialized countries are rent-seeking, market-
seeking, efficiency-seeking, and strategic-asset seeking factors. Brief descriptions of these
different factors are shown in Table 1 below.
FDI motivator Description
Rent-seekingInvolves foreign firms seeking cheaper factors and inputs of production, such as primary products.
Market-seeking
Involves foreign firms exporting or opening new markets in host countries in order to boost sales. This is also another way for firms to get around trade restrictions such as high transport costs and rules or origin.
Efficiency-seeking
Firms that aim at using a few countries to serve a larger market. The key motives for efficiency-seeking FDI are location, resource endowment, and government regulations.
Strategic-asset seeking
These firms are more concerned with maintaining the foreign firms' international position and competitiveness. In most low-income African countries, FDI is likely to fall in the non-market-seeking category.
Adapted from Kandiero & Chitiga (2006:356)
Table 1: FDI Motivators
Notwithstanding the arguments put forth by Kandiero and Chitiga, Musila and Sigué
(2006:577) argue that the role of FDI in the development of low-income countries is
controversial. The reasons cited for this contention are that FDI can have adverse effects on
employment, income distribution, and national sovereignty and autonomy.
If, for example, inputs need to be imported, the recipient country‟s balance of payments will
be adversely affected. The repatriation of profits to the investor‟s home country could also
potentially diminish foreign reserves. These fears led to the nationalization of many foreign-
owned corporations in the early years of independence in some African countries.
A case in point, highlighting some of the disadvantages of FDI, is Botswana (Gqubule, 2005).
Botswana has shown an impressive growth rate of approximately 10% per year for the past
8
few years, with falling foreign debt and single digit inflation. However, the country has an
unemployment rate of over 50% - even with vast sums of FDI flowing into the country from
South Africa, the United Kingdom, and Portugal. These inflows have not, however, made any
significant difference in the numbers of jobless; the problem being exacerbated by local
businesses being crowded out of the market by foreign companies.
To help understand the circumstances under which FDI may or may not assist in the
development process, Musila and Sigué (2006:579) identified three types of FDI. These are
extractive, market-seeking, and export-oriented FDI. Because of its abundance of raw
materials and natural resources, the bulk of FDI in Africa has, in the past, been of the
extractive kind. However, according to Musila and Sigué (2006), this type of FDI is
associated with high social costs in the form of:
exploitation of economic rent;
negative externalities in the form of pollution, and;
the exacerbation of inequality through dualistic economic structures.
Market-seeking FDI, on the other hand, may lead to conflict between private and social
benefits; especially if such FDI is protected from competition. For these reasons, most
governments, especially in Africa, have been attempting to attract export-oriented FDI. The
reason for this is that export-oriented FDI is unlikely to cause similar conflict as market-
seeking FDI (Musila and Sigué, 2006:579).
3.2. UNCERTAINTY AND FOREIGN DIRECT INVESTMENT
Froot and Stein (1991), Klein and Rosengren (1994), Bayoumi and Lipworth (1998),
Goldberg and Klein (1998), Ito (2000), Sazanami and Wong (1997), and Sazanami,
Yoshimura and Kiyota (2003) have all examined the effects of exchange rates on FDI (Kiyota
and Urata, 2004:1502).
According to Kiyota and Urata (2004:1502), only a few studies have focussed on the impacts
of exchange rate volatility on FDI. The findings of these studies are, however, mixed.
Cushman (1985 & 1988) and Goldberg and Kolstad (1995) for instance, found a positive
impact of exchange rate volatility on FDI, whilst Urata and Kawai (2000) and Bénassy-
Quéré, Fontagné and Lahrèche-Révil (2001) found a negative impact.
9
Xing and Wan (2006:425) contend that a depreciation of the recipient country‟s currency
stimulates FDI inflows to that country, with an appreciation leading to a reduction of FDI.
This is because a depreciation of the host country‟s currency results in a relative reduction in
the local production costs in relation to the foreign currency; resulting in increased profits.
The higher returns normally encourage additional investment, based on the expectation of
future high returns. Conversely, an appreciation of the host country‟s currency causes an
increase in operating costs, with concomitant lower profits and reduced investment.
However, Bénassy-Quéré, Fontagné and Lahrèche-Révil (2001) examined the trade-off
between exchange rate depreciation and its volatility in terms of their effects on FDI. They
argue that the negative impact on FDI of excessive volatility could erode the apparent
attractiveness resulting from currency depreciation (Xing and Wan, 2006:425).
When compared with studies analysing the effects of exchange rate and other variables, very
few studies have examined the impacts of exchange rate volatility on FDI. The effects of the
exchange rate on FDI are generally robust, as the depreciation of the host currency normally
promotes FDI inflows to that country. However, the impacts of exchange rate volatility on
FDI have been shown to be ambiguous (Kiyota and Urata, 2004:1506), as shown in Table 2
below.
10
Froot and Stein (1991) Klein and Rosengren (1994) Bayouni and Lipworth (1998) Goldberg and Klein (1998) Sazanami, Yoshimura and
Kiyota (2003)
Sazanami and Wong (1997)
Period Type 1974-1987
Annual/quarterly
1979-1991
Annual
1982-1995
Annual
1978-1994
Annual
1978-1999
Annual
1977-1992
Annual
Industry 13 industries All industry All industry All industry 8 industries All industry
Dependent Variable FDI Real inward FDI flows Real inward FDI flows Growth of real outward FDI
flows
Real inward FDI flows Real inward FDI flows Nominal outward FDI flows
FDI flows from Austria,
Australia, Belgium, Canada,
Denmark, Italy, Japan,
Netherlands, Norway, Spain,
Sweden, Switzerland and the
UK to the US divided by the US
GDP.
FDI flows from Canada, France,
Germany, Japan, Netherlands,
Switzerland and the UK to the
US divided by the US GDP
(natural log)
FDI flows from Japan to 20
major trading partners (natural
log)
FDI flows from Japan to
Argentina, Brazil, Chile,
Indonesia, Malaysia, Thailand
and the Philippines (natural log)
FDI flows from Japan to China,
Kong Kong, Indonesia, Korea,
Malaysia, Singapore, Taiwan,
the Philippines and Thailand
(natural log)
FDI flows from Japan to Asia,
the EC and United States
Independent Variables
Exchange Rate
Level negative
1/RER(US$/home): Index of
IMF merm real value of the
dollar
negative
RER(home/host)
negative
RER(home/host): WPI base
negative
RER(home/host): CPI base
positive
RER(host/home): CPI base
positive
NER(host/yen)
- positive positive negative negative -
- The standard deviation of
observed quarterly values of the
changes of RER within the year;
The average level of 'surprise'
during the year where surprise is
the deviation of the currently
observed changes in RER from
what was expected last quarter.
Short-run measure, which is the
mean of the four quarterly
values within the year of a
moving four-quarter s.d. of
RER; Long-run measure, which
is a moving 3-year s.d. of recent
annual changes in RER.
S.D. of RER over rolling
samples of 12 quarters of data,
prior to and inclusive of each
period t, normalised by the mean
level of the exchange rate within
the interval.
The coefficient of variation on
exchange rate (5 years average)
-
Other Variables
Demand - - - positive - positive
Level - - - - - -
Growth - - - - - -
Distance - - - - - -
Openness - negative - - positive positive
Relative labour cost - negative - - - -
Relative wealth - - negative - positive -
Cumulative FDI positive positive - - negative -
Trend
Notes:
RER: real exchange rate; NER: nominal exchange rate; host: the currency of recipient country; home: the currency of investing country
Major Regression Results of Previous Empirical Studies
Volatility
Kiyota and Urata (2004:1504)
Major results are reported. '-' indicates that variables are not included in the analysis
11
Ito (2000) Cushman (1985) Cushman (1988) Goldberg and Kolstad (1995) Bénassy-Quéré, Fontagné and
Lahrèche-Révil (2001)
Urata and Kawai (2000)
Period Type 1976-1996
Annual
1963-1978
Annual
1963-1986
Quarterly
1978:1-1991:4
Annual
1984-1996
Annual
1980-1994
Annual
Industry All industry All industry All industry All industry All industry 4 manufacturing industries
Dependent Variable FDI Nominal outward FDI flows Real FDI outflows Real inward FDI flows Real outward FDI stocks Real inward FDI stocks Location choice of Japanese
firms
FDI flows from Japan to Korea,
Taiwan, Hong Kong, Singapore,
Indonesia, Thailand, Malaysia,
the Philippines
Annual bilateral FDI flows from
the US to the UK, France,
Germany, Canada and Japan
Annual bilateral FDI flows into
the US from the UK, France,
Germany, Canada and Japan
Bilateral FDI flows between
Canada, Japan, the UK and the
US
FDI stock data for 42
developing countries from 17
OECD countries
117 countries chosen by
Japanese firms
Independent Variables
Exchange Rate
Level negative
NER(yen/US$) (lagged 1 year)
negative
RER(home/host): WPI
negative
RER(yen/host): GDP deflator
base
negative
RER(yen/host): PPI/WPI/CPI
base
negative
RER(yen/host): WPI base
negative
Index of NER(yen/US$)
- - - - - -
- - - - - -
Other Variables
Demand - positive positive positive - positive
Level positive - - positive - -
Growth - - - - - -
Distance - - - - negative -
Openness - - - - positive positive
Relative labour cost - - - - - -
Relative wealth - - - - - positive
Cumulative FDI negative - - - - -
Trend
Notes:
Major results are reported. '-' indicates that variables are not included in the analysis
RER: real exchange rate; NER: nominal exchange rate; host: the currency of recipient country; home: the currency of investing country
Major Regression Results of Previous Empirical Studies (cont.)
Volatility
Kiyota and Urata (2004:1504)
Table 2: Major Regression Results of Previous Empirical Studies
12
3.3. UNCERTAINTY AND THE REAL EXCHANGE RATE
A country‟s exchange rate is an important determinant of the growth of exports and imports.
In addition, it serves as a measure of international competitiveness, and is therefore a useful
indicator of economic performance. However, increasing exchange rate volatility is a major
source of exchange rate risk; which has significant and potentially negative implications for
the volume of trade flows and a country‟s balance of payments (Bah and Amusa, 2003:1).
Cushman (1985) analysed the connection between RER uncertainty and FDI, assuming
various relationships between foreign and domestic production. He concluded that, in
response to exchange rate risk, multinational firms reduce exports to the foreign country.
However, these firms offset this decrease in exports by increasing foreign capital input and
production (Lemi and Asefa, 2003:39).
Arize et al (2000:10) found that higher exchange-rate volatility leads to higher cost for risk-
averse traders, and to less foreign trade. This is because the exchange rate is agreed on at the
time of the trade contract, but payment is not made until the future delivery actually takes
place. If changes in exchange rates become unpredictable, this creates uncertainty about the
profits to be made and, hence, reduces the benefits of international trade.
Kyereboah-Coleman and Agyire-Tettey (2008:58) contend that depreciation of the currency
of the host country is likely to attract FDI inflows, for two reasons. Firstly, the currency
depreciation reduces production costs in the host country, providing an attractive proposition
to foreign companies.
Secondly, the currency depreciation lowers the value of assets in the host country in terms of
other currencies, including the currency of the source country. Accordingly, the cost of
undertaking FDI declines in terms of foreign currency, making FDI in the depreciating
currency country attractive. High volatility in the exchange rate is likely to discourage FDI
inflows because it increases uncertainty in the business environment in the host country.
13
4. THEORETICAL BACKGROUND
Having reviewed the literature associated with FDI flows and exchange rate uncertainty, this
section provides a more detailed analysis of the theory related to exchange rates. The
relationship between purchasing power parity and the RER is explored, followed by a
discussion of the South African RER. Particular focus is placed on the volatility of the South
African REER during the past thirty years.
A point to note is that, although the RER is discussed in this section, the analysis in the
succeeding sections will use the REER as an explanatory variable. Whereas the RER
compares the exchange rate between two countries, the REER compares the exchange rates
of a basket of currencies. The RER would therefore be used if the FDI from only one other
country was being evaluated. For this study, however, total FDI flow (from all sources) to
South Africa is considered. The REER therefore provides a more representative measure
from which to draw conclusions.
4.1. PURCHASING POWER PARITY AND THE REAL EXCHANGE RATE
Simply stated, the PPP theory proposes that, once converted to a common currency, national
price levels should be equal (Rogoff, 1996:647). According to Sarno and Taylor (2002:65),
PPP is defined as the exchange rate between two currencies that would equate the two
relevant national price levels, if expressed in a common currency at that rate. The purchasing
power of a unit of one currency would therefore be the same in both economies.
Sarno and Taylor (2002) go on to state that the above concept is often termed absolute PPP.
Relative PPP is said to hold when the rate of depreciation of one currency relative to another
matches the difference in aggregate price inflation between the two countries concerned. If
the nominal exchange rate is defined simply as the price of one currency in terms of another,
then the RER is the nominal exchange rate adjusted for relative national price level
differences. When PPP holds, the RER is a constant, so that movements in the RER represent
deviations from PPP. Hence, a discussion of the real exchange rate is tantamount to a
discussion of PPP (Sarno and Taylor, 2002:65).
The above view is supported by Cushman (1985:297). He contends that, when relative
inflation and exchange rate changes do not offset each other, deviations from PPP or
14
variations in the RER occur. The result of these variations is fluctuations in the real returns
from capital assets.
Akinboade and Makina (2006:44) contend that the PPP theory is important because most
theories of international finance are based on it. PPP is, for example, related to interest rate
parity and the international Fisher (IFE) theory. The former focuses on why forward rates
differ from the spot rate and the degree of difference that should exist, whilst the IFE focuses
on how the currency‟s spot rate will change over time.
A fundamental building block of PPP is the law of one price (LOP). Sarno and Taylor
(2002:66) define it in its absolute version as:
where ti ,P denotes the price of good i in terms of the domestic currency at time t, *
ti,P is the
price of good i in terms of the foreign currency at time t, and St is the nominal exchange rate
expressed as the domestic price of the foreign currency at time t.
According to equation (1), the absolute version of the LOP postulates that the same good
should have the same price across countries if prices are expressed in terms of the same
currency of denomination; the basic argument being contingent on the idea of frictionless
goods arbitrage (Sarno and Taylor, 2002:66).
The above contention is, however, simplistic. In reality, similar goods sold in different
locations have different prices. According to Engel and Roberts (1996:1112), citing several
studies performed by Engel (1993, 1995) and Rogers and Jenkins (1995), the movement of
prices of similar goods across borders account for much of the motion in RER. They contend
that the variation in these prices appears to be far more significant in explaining RER than are
movements in relative prices of different goods within a country‟s borders (such as non-
traded to traded goods prices).
Engle and Roberts‟ studies found that both the distance and the border played important roles
in the failure of establishing the LOP. The results of these studies suggest the need to
N.....(1),1,2,iPSP *
ittti,
15
incorporate the distance and border effects in the analysis of exchange rate volatility (Kiyota
and Urata, 2004:1502).
4.2. THE SOUTH AFRICAN REAL EXCHANGE RATE
Hartzenberg et al (2005:209) defines the RER as a measure of the nominal exchange rate,
adjusted for differences in the inflation rate between countries; obtained by deflating the
nominal exchange rate by the inflationary differential that exists between two countries.
The RER provides a measure of how much, on average, the cost of South African produced
goods and services will cost relative to a comparable basket of foreign goods and services,
and therefore of their competitiveness over time (Mtonga, 2006:1).
The mathematical definition of the RER is:
Where:
RER = Real Exchange Rate
e = Nominal Exchange Rate
Pw = World Prices
P = Domestic Prices
South Africa has experienced a number of fundamental changes in its foreign exchange
policy over the past thirty years, as shown in Table 3. After experiencing several periods in
which the exchange rate was pegged to either the British Pound or the United States Dollar,
the commercial and financial rates were temporarily unified during the period 1983 to 1985.
However, due to economic sanctions in 1985, foreign exchange controls were tightened,
leading to the reintroduction of the financial rand (FinRand). (Akinboade & Makina,
2006:350)
The FinRand system imposed limitations on the flow of South African currency out of the
country. The sale of any South African asset by a non-resident resulted in the creation of
FinRands, which could only be traded between non-resident sellers and buyers. In effect,
).....(P
PeRER w 2
16
ownership merely passed from one non-resident to another, with FinRands never actually
leaving South Africa.
Table 3: Regime changes in the South African foreign exchange market
Figure 2 below depicts South Africa‟s nominal and REER for the period 1980 to 2006.
Akinboade & Makina (2006:349)
17
Figure 2: South African Nominal and Real Effective Exchange Rates - 1970 to 2006
After appreciating strongly in the 1970‟s, from 1972 to 1983, the Rand collapsed in the early
1980‟s by 47% over a space of just two years between 1983 and 1985. Subsequently, it then
rebounded by 44% between 1985 and 1994, but weakened again by 41% between 1994 and
2001. By December 2003, the level of the REER index represented an appreciation of more
than 50% over its December 2001 level (Mtonga, 2006:2).
SA Nominal and Real Effective Exchange Rates
0
50
100
150
200
250
300
350
400
1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005
1990=
100
Nominal effective rateReal effective rate
Source: Standish (2007)
18
5. METHODOLOGY
The methodology followed is similar to that used by Cushman (1988), Xing and Wan (2006),
and Kyereboah-Coleman and Agyire-Tettey (2008). In this respect, the research can be
defined as a correlation study (Leedy & Ormond, 2005:180), which examines the extent to
which differences in one characteristic or variable are related to differences in one or more
other characteristics or variables.
Figure 3 depicts the methodology used.
Figure 3: Methodology Sequence
Development of the basic estimable model consists of identifying the explanatory variables
considered to influence FDI to South Africa. Having identified the variables, data will then be
Develop basic
estimable model
Volatility Measure
Estimation Procedure
Estimation of Volatility
Index
Unit Root
Test
Error Correction
Model
Engel-Granger two-step model
Data Collection
19
sourced, after which a volatility measure of the South African REER will be performed. For
the volatility measure, autoregressive conditional heteroskedasticity (ARCH) and generalized
autoregressive conditional heteroskedasticity (GARCH) analysis will be used.
Having developed the volatility measure, a three-part estimation procedure will be employed.
Firstly, an estimate of the volatility index will be determined. Thereafter, the stationarity of
the explanatory variables will be determined by means of unit root tests.
After confirming the acceptability of the variables for regression analysis, the analysis will be
concluded with an Engle-Granger two-step model. This model involves firstly running static
regressions to develop long-run models, followed by an error correction model (ECM) to
develop short-run models. The analysis culminates in a regression model incorporating both
the long and short-run determinants of the dependent variable.
The various steps will now be described in further detail.
5.1. DEVELOP BASIC ESTIMABLE MODEL
For the purpose of this report, the basic estimable model developed by Kyereboah-Coleman
and Agyire-Tettey (2008:59) will be used. The model is specified as
where
tLFDIT = foreign direct investment at time t
tLREER = real effective exchange rate at time t
tLEXVOL = real effective exchange rate volatility at time t
tLOPEN = openness of the economy at time t
tLPGDP = GDP per capita used as a proxy for the market size
1tLFDIT_1 = stock of foreign direct investment
tPLIN = political situation (instability) in the country at time t (i.e. a dummy equal to 1 for
democratically elected government, and 0 otherwise).
)3....(.εχPLINλLFDIT_1LPGDPLOPENδLEXVOLαLREERφLFDIT tt1tttttt
20
5.2. DATA COLLECTION
The data set comprises of quarterly observations, from the first quarter of 1980 to the fourth
quarter of 2006. Data was retrieved from the International Monetary Fund‟s Financial
Statistics online database, as well as from the South African Reserve Bank‟s online database.
The variables considered in this study are similar to those considered by Kyereboah-Coleman
and Agyire-Tettey (2008). However, for this study, the REER, as opposed to the RER, is
used. Unlike the RER, which compares the real exchange rate between two countries, the
REER is determined using a basket of currencies. By using the REER, the author believes
that the results obtained will be more representative of the effect of exchange rate volatility
on total FDI to South Africa.
Also, unlike Kyereboah-Coleman and Agyire-Tettey (2008), the natural logarithms of the
variables are used. The log transformation was applied to remove the effect of growth over
time in the variance of the data (Pindyck & Rubinfield, 1998:586), as well as the fact that the
log transformation makes evaluation of elasticities of the estimation results easier.
Table 4 provides a summary of the variables under consideration.
Variable Determination Source(s)
LFDITt Foreign Direct Investment at time t,
in logarithmic terms. Deflated by
the South African GDP deflator to
yield real FDI.
Reserve Bank of South Africa, data
set KBP5600J
LREERt Real Effective Exchange Rate of
the South African Rand (ZAR)
against the US Dollar (USD), in
logarithmic terms.
IMF Financial Statistics
LEXVOLt Volatility of the REER, estimated
by the ARCH method, in
logarithmic terms.
LOPENt The ratio of South African exports
to imports to GDP, in logarithmic
terms.
IMF Financial Statistics
LPGDPt South African per capita GDP,
obtained by dividing GDP (in 2000
prices) by the South African
population at the time, in
logarithmic terms. Per capita GDP
is used, in this study, as a proxy for
the market size.
IMF Financial Statistics
21
Variable Determination Source(s)
LFDIT_1t-1 The lag of FDI, considered as
indicative of the stock of FDI, in
logarithmic terms.
IMF Financial Statistics
PLINt A dummy variable, where 1
indicates a post-Apartheid
government, and 0 indicates a
government during Apartheid. The
dummy variable is used as a proxy
for political stability.
Table 4: Variables under consideration
5.3. DESCRIPTION OF VARIABLES
A more detailed description of each variable follows below.
Foreign Direct Investment (LFDIT)
The dependent variable is South African FDI (Figure 4). Annual total direct
investment data from the Reserve Bank‟s online database was first deflated by the
South African GDP deflator, obtained from the IMF database, and then converted to
quarterly values by dividing the deflated annual totals by four. The consumer price
index (CPI) could be used when determining real FDI. Typically, however, CPI is
thought of as weighting non-traded items such as consumer services fairly heavily,
whereas the GDP deflator will weight non-tradables in proportion to their importance
in expenditures in the aggregate economy (Chinn, 2006:119).
9.2
9.6
10.0
10.4
10.8
11.2
80 82 84 86 88 90 92 94 96 98 00 02 04 06
LFDIT
Figure 4: South African total FDI 1980Q1 - 2006Q4
22
Real Effective Exchange Rate (LREER)
The REER (Figure 5) is weighted according to trade between South Africa and its
largest trading partners. Depreciation in the local currency would result in relatively
lower local costs for overseas investors, resulting in increased FDI flows. The
relationship between FDI and the REER is therefore expected to be negative, with an
appreciation in the REER expected to result in a decrease in FDI.
4.2
4.4
4.6
4.8
5.0
5.2
5.4
80 82 84 86 88 90 92 94 96 98 00 02 04 06
LREER
Figure 5: South African REER 1980Q1 - 2006Q4
Volatility of the REER (LEXVOL)
The REER volatility estimate (Figure 6) is obtained from ARCH tests performed
using Eviews™ econometrics software. Volatility in the REER is expected to have a
negative relationship with FDI, for similar reasons stated above.
-6.5
-6.0
-5.5
-5.0
-4.5
-4.0
-3.5
80 82 84 86 88 90 92 94 96 98 00 02 04 06
LEXVOL
Figure 6: South African REER volatility 1980Q1 - 2006Q4
23
Openness of the South African economy (LOPEN)
Openness of the South African economy (Figure 7) is proxied by the ratio of exports
and imports to GDP. All time series variable data was obtained from the IMF online
database. The degree of openness of an economy is expected to have a positive
relationship with FDI inflows into a country, owing to little or no export/import
restrictions.
-1.1
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
80 82 84 86 88 90 92 94 96 98 00 02 04 06
LOPEN
Figure 7: South African Economy Openness 1980Q1 - 2006Q4
South African per capita GDP (LPGDP)
GDP per capita (Figure 8) is used as a proxy for the South African market size.
Kyereboah-Coleman and Agyire-Tettey (2008:59), citing Tsikata et al (2000),
contend that the larger the market size of a country, the more FDI it attracts. Per
capita GDP is therefore expected to have a positive relationship with FDI.
9.80
9.85
9.90
9.95
10.00
10.05
10.10
10.15
80 82 84 86 88 90 92 94 96 98 00 02 04 06
LPGDP
Figure 8: South African per capita GDP 1980Q1 - 2006Q4
24
Stock of FDI (LFDIT_1)
The lag of FDI (Figure 9) is used as a proxy for the total FDI stock in a country, and
is included to investigate the long-term effect of FDI. If, for example, an investment is
made in a country, the investing company normally receives assistance from its
„parent‟ firm, leading to the inflow of further investments into a country (Kyereboah-
Coleman and Agyire-Tettey, 2008:65). The stock of FDI is therefore expected to have
a positive relationship with FDI.
9.2
9.6
10.0
10.4
10.8
11.2
80 82 84 86 88 90 92 94 96 98 00 02 04 06
LFDIT_1
Figure 9: South African FDI stock 1980Q1 - 2006Q4
Political Stability (PLIN)
In order to assess the effect of political influence on FDI to a country, a dummy
variable is included as one of the explanatory variables. In this case, a 1 signifies a
post-Apartheid democratically elected government, with a 0 indicating a non-
democratically elected government during the Apartheid years. Political instability is
expected to have a negative impact on FDI flows to a country.
5.4. VOLATILITY MEASURE
According to Bah and Amusa (2003), most previous studies have measured exchange rate
volatility using the sample standard deviation method. This method uses a time-varying
measure of exchange-rate volatility to account for periods of low and high exchange-rate
uncertainty. This proxy is constructed by the moving-sample standard deviation (Arize et al,
2000:11).
25
Bah and Amusa (2003) contend that the standard deviation method has two distinct
drawbacks, viz. (1) it wrongly assumes that the empirical distribution is normal, and (2) it
ignores the distinction between predictable and unpredictable elements in the exchange rate
process.
A popular method used for estimating financial volatility is called autoregressive conditional
heteroskedasticity (ARCH) and generalized autoregressive conditional heteroskedasticity
(GARCH). These models were introduced by Engle (1982) and Bollerslev (1986),
respectively, and have been shown to be best suited to exchange rates; which have been
known to follow a GARCH process (McKenzie, 1999).
ARCH models are specifically designed to model and forecast conditional variances. The
variance of the dependent variable is modeled as a function of past values of the dependent
variable and independent, or exogenous, variables (Eviews 6 manual).
The GARCH model is an extension of the ARCH, only adding lags of the volatility measure
itself – instead of only adding lags of squared errors. The properties of the GARCH are
therefore similar to those of the ARCH model. However, the GARCH model is much more
flexible in being capable of matching a wide variety of patterns of financial volatility (Koop,
2006:204).
Koop (2006), provides a succinct analysis of volatility measurement, by considering the
familiar regression model
)4.....(X...XY tktk11t
If X1t=Yt-j, i.e. the explanatory variables are lags of the dependent variable, this is an
autoregressive (AR) model. As the name suggests, an autoregressive model is a regression
model where the explanatory variables are lags of the dependent variable (Koop, 2006:181).
The ARCH model relates to the variance (or volatility) of the error, εt, where volatility is
)).....(var( t 52
2 is therefore volatility at time t. Because the error variances differ across observations, the
assumption that the error variances are part of the same distribution cannot be made. The
26
model is therefore said to be heteroskedastic, which accounts for the „H‟ in ARCH. In
essence, therefore, the ARCH model assumes that today‟s volatility is an average of past
errors.
5.5. ESTIMATION PROCEDURE
This section consists of three parts, viz. the estimation of the volatility index, unit root tests to
determine if the time series data variables are stationary, and error correction modeling in the
event that the variables are not stationary in levels. Following below, a brief description of
the above-mentioned tests.
5.5.1. Estimating the Volatility Index
According to Kyereboah-Coleman and Agyire-Tettey (2008:62), the REER volatility measure
is defined by the following relationship
).....(eREERlnREERln tt 61
where et ≈ N (0,ht), and:
).....(heh tttt 72
1
2
1
The conditional variance (ht; equation (7)) is a function of three terms, viz:
1) The mean, ;
2) Information about previous volatility, measured as the lag of the squared residual
from the mean equation 21te (the ARCH term), and;
3) The previous forecast error variance, 1th (which is the GARCH term).
The exchange rate volatility derived in GARCH is conditional on past information, and
therefore reflects the actual volatility perceived by investors.
5.5.2. Unit Root Tests
A key consideration when conducting regression analysis is whether or not the variables
under consideration revert back to a long-term mean level after a shock, or if the variables
follow a random walk. In the latter case, variables are referred to as non-stationary, with a
27
regression of one variable against another leading to spurious regression results (Pindyck &
Rubinfeld, 1998:507).
Before running a regression, the variables must therefore be tested to ascertain whether they
are stationary. In the event that variables are found to be non-stationary, such variables can be
differenced to make them stationary, and thus acceptable for regression analysis. When a
series is stationary in level, it is said to be integrated of order zero (I(0)), and when it is
integrated of a higher order, it is differenced in order to become stationary (Kyereboah-
Coleman and Agyire-Tettey, 2008:63).
Typically, the Augmented Dickey-Fuller (ADF) test is used to test for the presence of unit
roots. To test a variable Yt for a unit root, the following regression equation is estimated
).....(Y YtY tpttt 81210
where the first difference of Yt is regressed against a constant, a time trend (t = 1,2,...,T), the
first lag of Yt, and, if necessary, lags of ΔYt. Sufficient lags of ΔYt must be included to ensure
no autocorrelation in the error term. To be certain, the normal Lagrange Multiplier (LM) test
for autocorrelation should be conducted to confirm that autocorrelation is not present. If it is,
extra lags of ΔYt should be added until the autocorrelation disappears.
The test for a unit root, i.e. non-stationarity, is based on the t-stat on the coefficient of the
lagged dependent variable, Yt-1. If this is greater than the critical value, then the null
hypothesis of a unit root is rejected, and the variable is taken to be stationary (Beachill,
2007).
5.5.3. Error Correction Model
According to Engle and Granger (1987:252), provided that variables are co-integrated, error-
correcting models allow long-run components of variables to obey equilibrium constraints,
while short-run components have a flexible dynamic specification. If the dependent variable
is above its equilibrium level, it will start returning to equilibrium in the next period. The
equilibrium error will therefore be „corrected‟ in the model (Koop, 2006:225).
28
Stated simply, an ECM provides a way of combining the long run, cointegrating relationship
between the level variables and the short run relationship between the first differences of the
variables (Beachill, 2007).
When any of the variables are not stationary in levels, a cointegration test is performed. This
test entails estimating the variables in their levels, in order to generate the residuals from the
estimation. The residuals are then tested for unit roots using the ADF test. If the residuals are
found to be stationary in levels, the variables are cointegrated, signifying that the short-run
and long-run behaviour of the dependent variable is tied together (Kyereboah-Coleman and
Agyire-Tettey, 2008:63).
The ECM is best explained by the following equation
).....(eXY tttt 901
where 1t is the error obtained from the cointegrating regression (i.e. 1t = 1t1t XY )
and et is the error in the ECM. If 1t is known, then the ECM would be just a regression
model. can therefore be thought of as an equilibrium error. If it is non-zero, then the model
is out of equilibrium, with equilibrium errors being magnified instead of corrected. Such
behaviour is inconsistent with cointegration (Koop, 2006:224).
5.6. ENGLE-GRANGER TWO-STEP MODEL
Upon confirmation of both unit roots and cointegration of the time series variables, the
Engle-Granger two-step model can be applied. According to Stewart (2005:829), this method
is the best-known method for estimating all the parameters of the ECM in a way that does not
require nonlinear least squares regression. The method involves firstly running a static
regression to generate residual series relevant for the error correction process, utilising
ordinary least squares (OLS) regression (Kyereboah-Coleman and Agyire-Tettey, 2008:64).
Similar to Bah and Amusa (2003), a “general to specific” approach is adopted. In this
approach, an over-parameterized model is estimated. The model is then stepwise reduced by
eliminating insignificant lagged variables, until a parsimonious model is obtained. In this
29
case, parsimony refers to obtaining a model with only one statistically significant explanatory
variable – the variable being either lagged or normal. The lag reduction is principally guided
by statistical and economic considerations.
Having reached parsimony, the second step of the Engle-Granger method involves saving the
residual series of the parsimonious model, and running a second OLS of the differenced
explanatory variables. However, in the second regression, the first lag of the residual error
series is included as an explanatory variable. In this manner, the short-run behaviour is
partially captured by the equilibrium error term, which says that, if Y is out of equilibrium, it
will be “pulled back” towards equilibrium in the next period (Koop, 2006:225).
30
6. FINDINGS
This section documents the findings of the various econometric and statistical analyses. It
commences with the results of the determination of the REER volatility, using ARCH and
GARCH methods discussed in the preceding section.
Thereafter, the findings of the cointegration analysis of the explanatory variables are
presented. A summary of the results of the unit root tests, performed using the augmented
Dickey-Fuller (ADF) test, is tabled. Detailed results of these tests are included in Appendices
1 and 2 for comprehensiveness.
As an additional verification, cointegration tests based on the residuals of a normal ordinary
least squares regression is included. For all the tests, cointegration of the explanatory
variables is confirmed.
Upon confirmation of stationarity of the explanatory variables, the long-run relationship can
be established using ordinary least squares regression analysis. With the explanatory
variables being stationary, the potential for spurious regression results is reduced.
Based on the fact that the per capita GDP variable was found to be non-stationary both in
level and first-differenced terms, two different regression models are run. The first model
includes this variable, whilst the second model excludes it.
Having developed the long-run models, the lagged residual series, generated from the long-
run model, is then used as an explanatory variable in an error correction model. The error-
correction method is applied to determine the short-run characteristics of the explanatory
variables.
Similar to the long-run models, two models – one including and the other excluding the per
capita GDP variable – are run. However, due to autocorrelation in Model 1, the results of
both the long-run and short-run analyses for this model are considered to be spurious. The
result of this model is included only for the sake of completeness.
For Model 2, the error correction term is found to be negative, and statistically significant at
the 1% level of significance. This result confirms the accuracy of the model, as the negative
31
error corrrection term means that the dependent variable will tend to return to equilibrium in
the short-term.
6.1. EXCHANGE RATE VOLATILITY
Table 5 below lists the results of the ARCH test performed to determine the volatility of the
South African REER during the period 1980 to 2006.
Dependent Variable: LREER Method: ML - ARCH (Marquardt) - Normal distribution Date: 11/10/08 Time: 13:24 Sample (adjusted): 1980Q2 2006Q4 Included observations: 107 after adjustments Convergence achieved after 24 iterations Presample variance: backcast (parameter = 0.7) GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1)
Variable Coefficient Std. Error z-Statistic Prob. C 0.325494 0.108770 2.992506 0.0028
LREER(-1) 0.931710 0.022442 41.51570 0.0000 Variance Equation C 0.001625 0.000369 4.401924 0.0000
RESID(-1)^2 0.593162 0.214382 2.766852 0.0057 GARCH(-1) -0.024796 0.085470 -0.290109 0.7717
R-squared 0.916241 Mean dependent var 4.783353
Adjusted R-squared 0.915443 S.D. dependent var 0.203541 S.E. of regression 0.059187 Akaike info criterion -2.998396 Sum squared resid 0.367824 Schwarz criterion -2.873497 Log likelihood 165.4142 Hannan-Quinn criter. -2.947764 F-statistic 287.1488 Durbin-Watson stat 1.668965 Prob(F-statistic) 0.000000
Table 5: Estimation of REER volatility
The results indicate that the South African REER volatility follows an ARCH process. This
is evident by the statistically significant (at the 1% level of significance) ARCH (resid(-1)^2)
term.
The REER volatility is therefore classified as an ARCH (1) process, where ARCH (1) refers
to an ARCH model with one lagged variable. Equation (7) is therefore specified as a first-
32
order ARCH model. Based on the statistically significant ARCH term, the extension to the
GARCH model is therefore not necessary.
The mean and variance equations from the test are specified in equations (10) and (11)
respectively
)).....((REERln..REERln 10193203250
).....(h.e..h ttt 110248059300016250 2
1
2
1
The point estimates in equation (11) summarize the short-run properties of the REER
volatility. The coefficient on 2
1te of 0.593 measures how much the REER responds to
equilibrium errors, i.e. volatility in the previous period. The coefficient on 2
1th (the GARCH
term) represents the previous period‟s forecast variance.
Since the ARCH term is positive, positive errors tend to cause REER volatility to be positive
and, hence, to increase. In this case, an equilibrium error of 1% will cause a 0.59% increase
in the REER volatility in the next period.
6.2. UNIT ROOT AND COINTEGRATION TESTS
Having obtained the REER volatility variable to be used in the ECM, unit root tests using the
ADF test were conducted on the variables to determine the order of integration. Appendix 1
and 2 presents detailed results for the unit root analysis. For the sake of brevity, a summary of
the unit root test results for both level and first differenced time series is shown in Table 6
below.
33
Variable t-ADF (Without trend)
Null Hypothesis: Variable has a unit root
In levels t-statistic 1% level 5% level 10% level
LFDIT 1.846468 -2.5867 -1.9438 -1.6147
LREER -0.964615 -2.5874 -1.9439 -1.6147
LPGDP 1.348895 -2.5895 -1.9442 -1.6145
LFDIT_1 1.847872 -2.5869 -1.9438 -1.6147
LOPEN -1.364819 -2.5874 -1.9439 -1.6147
LEXVOL -0.277124 -2.5876 -1.9439 -1.6147
In first differences t-statistic 1% level 5% level 10% level
DLFDIT* -10.24695 -2.5867 -1.9438 -1.6147
DLREER* -4.719506 -2.5873 -1.9439 -1.6147
DLPGDP -1.229443 -2.5897 -1.9443 -1.6144
DLFDIT_1* -10.19804 -2.5871 -1.9439 -1.6147
DLOPEN* -10.33957 -2.5874 -1.9439 -1.6147
DLEXVOL* -9.9282 -2.5876 -1.944 -1.6147
Notes: All variables are as defined in equation (3), expressed
in logarithms. *, ** and *** denotes rejection of the unit root
hypothesis at the 1%, 5% and 10% level of significance respectively.
Table 6: ADF unit root test results
As seen in Table 6, in level terms, the null hypothesis of a unit root is accepted for all the
variables, as the t-statistic does not fall within the rejection area. In first difference terms,
with the exception of the logarithm of per capita GDP, all the remaining variables are
stationary, i.e. no unit root, at the 1% level of significance.
Figure 10 provides a graphical representation of the explanatory variables in first difference
terms. With the exception of the per capita GDP variable (LPGDP), the remaining variables
all exhibit a random walk pattern, with a reversion to a mean level. The variables are
therefore considered to be stationary in first differenced terms.
34
-.4
-.2
.0
.2
.4
.6
1980 1985 1990 1995 2000 2005
DLFDIT
-.3
-.2
-.1
.0
.1
.2
.3
1980 1985 1990 1995 2000 2005
DLREER
-.06
-.04
-.02
.00
.02
.04
1980 1985 1990 1995 2000 2005
DLPGDP
-.3
-.2
-.1
.0
.1
.2
1980 1985 1990 1995 2000 2005
DLOPEN
-3
-2
-1
0
1
2
3
1980 1985 1990 1995 2000 2005
DLEXVOL
Figure 10: ADF unit root test graphical output
The per capita GDP variable (LPGDP) has an upward trend from the period 1994 onwards,
hence the presence of a unit root even in first difference terms This finding is, however,
consistent with Koop‟s (2006:179) assertion that macroeconomic time series such as income,
GDP and consumption change only slowly over time. Consequently, this quarter‟s income
tends to be quite similar to last quarter‟s, and thus they are highly correlated with one
another.
Having confirmed the stationarity of the first differenced variables, the only remaining
criterion for acceptance of an ECM is that of cointegration between variables. Considering
that the term DLPGDP contains a unit root at first difference, two cointegration tests and
ECM‟s will be run to test the sensitivity of the model. One set of tests will contain the
LPGDP term; with the other set omitting the LPGDP term. The results of the cointegration
tests are shown below in Table 7 and Table 8 respectively.
35
Date: 11/14/08 Time: 08:40 Sample: 1980Q1 2006Q4 Included observations: 100 Series: DLFDIT DLREER DLPGDP DLOPEN DLEXVOL PLIN Lags interval: 1 to 4
Selected (0.05 level*) Number of Cointegrating Relations by Model
Data Trend: None None Linear Linear Quadratic
Test Type No Intercept Intercept Intercept Intercept Intercept No Trend No Trend No Trend Trend Trend
Trace 4 3 3 3 4 Max-Eig 3 2 2 2 2
*Critical values based on MacKinnon-Haug-Michelis (1999)
Table 7: Model 1 cointegration test (including LPGDP)
Date: 11/14/08 Time: 08:41 Sample: 1980Q1 2006Q4 Included observations: 100 Series: DLFDIT DLREER DLOPEN DLEXVOL PLIN Lags interval: 1 to 4
Selected (0.05 level*) Number of Cointegrating Relations by Model
Data Trend: None None Linear Linear Quadratic
Test Type No Intercept Intercept Intercept Intercept Intercept No Trend No Trend No Trend Trend Trend
Trace 3 2 3 2 3 Max-Eig 4 1 1 1 1
*Critical values based on MacKinnon-Haug-Michelis (1999)
Table 8: Model 2 cointegration test (excluding LPGDP)
As a means of confirmation of cointegration amongst variables, an additional cointegration
test was performed. This test entails running an OLS regression on the first differenced
variables in question. An ADF test on the residual series generated by the OLS is then
performed, to determine whether the disturbances in a regression contain a stochastic trend. If
the residual is found to be stationary, the time series variables are cointegrated
(Murray, 2006:793). The results of the cointegration test are shown below.
36
Dependent Variable: DLFDIT Method: Least Squares Date: 11/14/08 Time: 08:44 Sample (adjusted): 1980Q3 2006Q3 Included observations: 105 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. DLREER -0.048290 0.145958 -0.330849 0.7415
DLOPEN -0.037007 0.160306 -0.230854 0.8179 DLEXVOL -0.023980 0.012865 -1.863983 0.0653 DLFDIT_1 -0.041236 0.099971 -0.412479 0.6809
PLIN -0.000305 0.016977 -0.017981 0.9857 C 0.017057 0.011791 1.446650 0.1512 R-squared 0.037935 Mean dependent var 0.016197
Adjusted R-squared -0.010654 S.D. dependent var 0.086131 S.E. of regression 0.086589 Akaike info criterion -1.999852 Sum squared resid 0.742261 Schwarz criterion -1.848198 Log likelihood 110.9923 Hannan-Quinn criter. -1.938399 F-statistic 0.780733 Durbin-Watson stat 2.028423 Prob(F-statistic) 0.565921
Null Hypothesis: RESID04 has a unit root Exogenous: None Lag Length: 0 (Automatic based on AIC, MAXLAG=12)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic -10.29796 0.0000
Test critical values: 1% level -2.587387 5% level -1.943943 10% level -1.614694 *MacKinnon (1996) one-sided p-values.
The results indicate that, at the 1% level of significance, the null hypothesis of a unit root in
the residual series is rejected, as the ADF test statistic of -10.298 falls within the rejection
region of -2.587. The absence of a unit root in the residual series indicates cointegration
amongst the variables.
Based on the results of the ADF tests of Table 7 and Table 8, as well as the supplementary
cointegration test for stationarity in the residual series, it can therefore be concluded that
there exist at least one cointegrating relationship among the variables of equation (3). What
this means is that, amongst a combination of the non-stationary random variables, a
stationary linear relationship exists. The necessary conditions for the use of an ECM therefore
exists (Bah and Amusa, 2003:13).
37
6.3. ENGLE-GRANGER STEP ONE: LONG-RUN RELATIONSHIP MODEL
The results of the long-run relationship between FDI and the explanatory variables for Model
1 and 2 are shown in Appendices 3 and 4 respectively. From the OLS regressions, the
following long-run relationships are developed.
6.3.1. Long-Run Model 1
The long-run Model 1 regression equation is presented below, with Figure 11 showing the
graphical output of the actual and fitted values. The t-statistic values are shown in
parentheses.
0.8698WatsonDurbin;427.10Mean;1119.0errorStd0.877;2
RAdj0.885;2
R
)12......()12(PLIN067.0_1(-2)0.679LFDIT
(-5)0.688LPGDP(-3)0.635LOPENL0.073LEXVO--10)0.31LREER(956.4LFDIT
]242.1[[9.480]
[2.533][4.785][-3.635][3.838]]577.1[
-.3
-.2
-.1
.0
.1
.2
.3 9.6
10.0
10.4
10.8
11.2
84 86 88 90 92 94 96 98 00 02 04 06
Residual Actual Fitted
Figure 11: Long-Run Model 1 Actual vs Predicted
Overall, the model explains 88.5% of the variation in FDI inflows, suggesting a good fit
between the predicted and actual values. Also, the low standard error of 0.1119, compared to
a mean of 10.427, demonstrates a good fit. However, the low Durbin-Watson statistic is
38
evidence of first-order autocorrelation between variables, which can result in spurious
regression results. Any further discussion on the results of long-run Model 1 would therefore
be meaningless.
As noted previously, the null hypothesis of a unit root for the per capita GDP variable
(LPGDP) cannot be rejected, even in first difference terms. The presence of autocorrelation
in Model 1 can therefore most likely be ascribed to the presence of the LPGDP variable. As a
result, Model 2, which excludes this variable, was run. Results for this model follow below.
6.3.2. Long-Run Model 2
The long-run Model 2 regression equation is presented below, with Figure 12 showing the
graphical output of the actual and fitted values. The t-statistic values are shown in
parentheses.
949.1WatsonDurbin;427.10Mean;0805.0errorStd0.936;2
RAdj0.939;2
R
)13......()12(PLIN026.0
_10.852LFDIT(-10)0.166LOPENL(-9)0.021LEXVO54.1LFDIT
]586.0[
[18.731][1.411][-1.654]]045.3[
-.4
-.2
.0
.2
.4 9.6
10.0
10.4
10.8
11.2
84 86 88 90 92 94 96 98 00 02 04 06
Residual Actual Fitted
Figure 12: Long-Run Model 2 Actual vs Predicted
39
From Figure 12, Model 2 appears to be a better model for describing the variation in FDI
inflows. The relatively high R2 suggests that the model is a good fit, explaining 94% of the
variation in FDI inflows. The low standard error of 0.0805, compared to the mean of 10.427,
also attests to the good fit. Also, the Durbin-Watson statistic of 1.94 indicates that the
autocorrelation between variables, as seen in Model 1, has been removed with the omission
of the non-stationary LPGDP variable.
The expected signs of the long-run Model 2 coefficients conform to a priori expectations. A
notable exclusion from Model 2 is the REER variable (LREER), which was found to be
statistically insignificant at all lag levels during the step-wise reduction process shown in
Appendix 4.
As expected, REER volatility (LEXVOL) has a statistically significant (at the 5% level of
significance) negative relationship with FDI inflows. This means that FDI is more likely to
flow to countries with a stable exchange rate. The primary concern related to volatile
exchange rates is that the volatility can cause income fluctuations when goods or services are
imported or exported. Investor risk therefore increases, as fluctuating earnings or income can
negatively affect future cash flows and investor sentiment.
The lag of 9 quarters suggests that the impact of REER volatility on FDI is only witnessed
two years after the event. Considering that FDI inflow normally constitutes fixed capital
investment, with commensurate long lead times, the lagged effect of REER volatility is not
surprising.
Openness of the economy (LOPEN) is found to have a statistically significant (at the 10%
level of significance) positive relationship with FDI inflows. South Africa‟s open market
policies therefore positively influence FDI inflows into the country, as investors can be
confident that they will be able to buy and/or sell the products and services originating in
South Africa in other markets. However, the low significance level would tend to suggest
that, for this model, openness of the economy would be of lower importance in the
consideration to invest.
The stock of current FDI, proxied by the lag of FDI (LFDIT_1), has a statistically significant
(at the 1% level) positive relationship with FDI inflows. According to the model, the current
40
stock of FDI has a positive effect on the decision to invest. Investment decisions are therefore
based not only on the market size, but also on the amount of FDI already present in a country.
The amount of existing FDI is therefore a strong determinant in the decision to invest in a
country.
Political instability (PLIN) has a positive relationship to FDI inflows. This means that FDI is
more likely in a country with a stable political environment, which reduces an investor‟s
sovereign risk component. However, its impact is not significant, as evidenced by the low
statistical significance. Based on the regression, investors appear to consider the other
explanatory variables more important than the country‟s political situation (at the time) in
their decision to invest.
6.4. ENGLE-GRANGER STEP TWO: ERROR CORRECTION MODEL
Having considered the long-run determinants of FDI in the previous section, the ECM is used
to capture the short-run phenomenon of the time series model (Kyereboah-Coleman and
Agyire-Tettey, 2008:63). The ECM is specified as follows
)14).....(1(ECMPLIN
FDITPGDPLOPENLEXVOLLREERLFDIT
6
1T543210
where Δ implies differencing, and the ECM represents the error correction term (Kyereboah-
Coleman and Agyire-Tettey, 2008:63). The ECM is obtained by saving the residual series of
the OLS, after parsimony has been reached by step-wise reduction of statistically
insignificant lagged variables. The lag of the error term is then included as a variable in a
second OLS. For the purpose of this study, the expected sign of the coefficient, as well as the
value of the t-statistic at the 10% significance level, is used for deciding which variables to
remove.
The ECM Model 1 (Appendix) 3 contains the results of step-wise reductions of the lagged
and differenced variables, where the variable LPGDP is included. Model 2 (Appendix 4)
presents the results of similar tests. However, the variable LPGDP, in which the null
hypothesis of a unit root could not be rejected, is omitted from the ECM.
41
6.4.1. ECM Model 1 Results
The results of the ECM for Model 1 are shown Table 9 below.
Variable Coefficient Std. Error t-Statistic Prob. C 0.006788 0.011240 0.603922 0.5475
DLREER(-10) 0.075148 0.136463 0.550682 0.5833 DLOPEN(-3) 0.365624 0.159171 2.297056 0.0240 DLEXVOL -0.033597 0.012348 -2.720919 0.0079
DLPGDP(-5) -0.263793 0.651960 -0.404615 0.6868 DLFDIT_1(-2) 0.232852 0.114200 2.038983 0.0445
PLIN(-12) 0.005040 0.017605 0.286300 0.7753 ECM1_1(-1) -0.303095 0.088365 -3.430047 0.0009
Table 9: ECM results for Model 1
As demonstrated in §6.2, the per capita GDP variable has a unit root even in first difference
terms. The long-run Model 1 results are therefore considered to be spurious, with the results
of the ECM for model 1 only included for the sake of completeness.
6.4.2. ECM Model 2 Results
The results of the ECM for Model 2 are shown Table 10 below.
Variable Coefficient Std. Error t-Statistic Prob. C 0.000131 0.011380 0.011481 0.9909
DLOPEN(-10) 0.133368 0.155559 0.857344 0.3936 DLEXVOL(-9) -0.026785 0.012366 -2.166035 0.0330
DLFDIT_1 0.974224 0.267069 3.647834 0.0004 PLIN(-12) -0.000997 0.016783 -0.059390 0.9528
ECM2_2(-1) -1.109309 0.283084 -3.918650 0.0002
Table 10: ECM results for Model 2
The magnitude of the error correction term indicates the change in FDI per quarter that is
attributed to the disequilibrium between the actual and equilibrium levels (Takaendesa et al,
2006:93). The ECM is the speed of adjustment to the long run equilibrium (Kyereboah-
42
Coleman and Agyire-Tettey, 2008:65), and is negative and statistically significant at the 1%
level. This confirms the existence of cointegration between variables.
The stock of FDI variable (DLFDIT_1) is statistically significant at the 1% level. The a priori
expected sign confirms that investors consider the current FDI investment in a country as an
important factor in the decision to invest.
As with the long-run Model 2, the REER volatility variable (DLEXVOL) has the a priori
expected sign, and is statistically significant at the 5% significance level. This result confirms
that REER volatility plays an important role in the decision to invest. The negative sign
indicates that REER volatility impacts negatively on FDI investment.
43
7. DISCUSSION OF FINDINGS
The central hypothesis for this research is:
Volatility in the South African REER has impacted negatively on FDI during the past
twenty years.
The results of the long-run regression analysis model (Model 2) indicate that volatility in the
REER has impacted negatively on FDI. The REER volatility variable (LEXVOL) is
statistically significant, and negative in sign. The null hypothesis is therefore accepted.
The results of the short-run model support the findings of the long-run Model 2. The error
correction term is negative, and statistically significant. This means that for South Africa,
FDI flows restore to a long-run equilibrium level in a relatively short time period. This effect
is evident from Figure 1, which shows that FDI flows „normalised‟ within one year following
significant FDI inflows due to corporate activities associated with De Beers in 2001, and
ABSA in 2005.
The long-run Model 1 displayed a very low Durbin-Watson statistic, which is evidence of
autocorrelation between the variables. There is therefore a very strong possibility of spurious
regression results, as a result of the inclusion of the non-stationary per capita GDP variable
(LPGDP).
Long-run Model 2, which excludes the non-stationary per capita GDP term, confirmed the
spurious regression concerns related to long-run Model 1. For Model 2, the REER variable
(LREER) was found to be statistically insignificant at all lag levels. With a statistically
significant REER volatility variable (LEXVOL), this model suggests that investors are more
concerned about REER volatility than the REER level itself when deciding to invest in a
country.
In a study of RER volatility on international trade, Dellas and Zilberfarb (1993) contend that
because most trade contracts are not for immediate delivery of goods, unanticipated
fluctuations in the exchange rate affect realized profits.
44
Extrapolating from this contention, the argument could be made that, because FDI normally
entails fixed capital investment with relatively long lead times before profits are realized, the
current level of the exchange rate at the time the investment is made is not as important a
consideration in the decision to invest. Rather, volatility in the exchange rate, which could
influence future profits either positively or negatively, would be a more important
consideration.
With a relatively volatile exchange rate, the exchange rate level at the time the investment is
made would not be an accurate or reliable indicator of the exchange rate at the time when
future profits are realized. Investor uncertainty, and therefore risk, would therefore increase
due to the volatile exchange rate. This contention would account for the statistical
significance of the REER volatility variable (LEXVOL), and the omission of the REER
variable (LREER) from the more accurate long-run Model 2.
45
8. CONCLUSION
The objective of this study was to determine if volatility of the South African REER
negatively impacted on foreign direct investment into South Africa during the period 1980 to
2006. Using quarterly data from the International Monetary Fund‟s International Financial
Statistics database, a similar methodology as used by Kyereboah-Coleman and Agyire-Tettey
(2008) was employed.
This approach included determining the South African REER volatility index for the period
1980Q1 to 2006Q4, using an autoregressive conditional heteroskedastic approach. Both long-
run and short-run models were then developed to investigate the impact of several time-series
variables on FDI inflows to South Africa.
In order to account for a unit root in the per capita GDP variable, two different econometric
models were run. Long-run Model 1 included the per capita GDP variable, and long-run
Model 2 omitted the variable. Based on the Engle-Granger representation theorem, an error
correction process was used to test the short-run error correction relationship associated with
time-series variables that are not stationary in level terms.
The results of both the long-run and short-run Model 2, particularly the short-run model‟s
error correction term‟s sign and statistical significance, supports the hypothesis that REER
volatility negatively impacted on South African FDI during the period 1980 to 2006.
The most notable result obtained from the regression analysis is the omission of the REER
level as a consideration in the FDI decision. The argument is made that REER volatility,
rather than REER levels at the time the decision is made to invest, plays a more important
role in the investment decision.
With relatively long lead times, FDI profits are normally realized sometime in the medium to
long-term future. As a result of exchange rate volatility, the current exchange rate level is not
an accurate or predictable indicator of future exchange rates. This uncertainty with respect to
exchange rate fluctuations on future profits therefore increase investor risk, which has a
negative effect on the initial decision to invest.
46
9. BIBLIOGRAPHY
9.1. JOURNALS
Akinboade, OA and Makina, D (2006), Purchasing Power Parity and the Real Rand-Dollar
Exchange Rate, Journal of Studies in Economics and Econometrics, Vol 30, No 3.
Akinboade, OA and Makina, D (2006), Mean Reversion and Structural Breaks in Real
Exchange Rates: South African Evidence, Applied Financial Econometrics, 16, 347-358.
Arize, AC, Osang, T & Slottje, DJ (2000), Exchange Rate Volatility and Foreign Trade:
Evidence from Thirteen LDC‟s, Journal of Business & Economic Statistics, Vol. 18, No. 1,
10-17.
Bah, I and Amusa, HA (2003), Real Exchange Rate Volatility and Foreign Trade: Evidence
from South Africa‟s Exports to the United States, The African Finance Journal, Vol 5,
Part 2.
Bénassy-Quéré, A, Coupet, M and Mayer, T (2007), Institutional Determinants of Foreign
Direct Investment, The World Economy, Blackwell Publishing, Oxford, United Kingdom.
Chinn, MD (2006), A Primer on Real Effective Exchange Rates: Determinants,
Overvaluation, Trade Flows and Competitive Devaluation, Open Economies Review, 17,
115-143.
Cushman, DO (1985), Real Exchange Rate Risk, Expectations, and the Level of Direct
Investment, The Review of Economics and Statistics, Vol 67, No 2.
Dass, PS (2006), “The Convergence of Capital Market and Money Market Interest Rates”,
MBA Thesis, University of Cape Town, Graduate School of Business, Cape Town, South
Africa.
Dellas, H and Zilberfarb, B-Z (1993), Real Exchange Rate Volatility and International Trade:
A Re-examination of the Theory, Southern Economic Journal, Vol. 59, No. 4, 641-647.
47
Engel, C and Rogers, JH (1996), How Wide is the Border?, The American Economic
Review, Vol 86, No 5.
Engle, RF and Granger, CWJ (1987), Co-Integration and Error Correction: Representation,
Estimation, and Testing, Econometrica, Vol 55 No 2.
Gqubule, T (2005), Foreign direct investment crowds out local business, Financial Mail, 25
February 2005.
Kandiero, T & Chitiga, M (2006), Trade Openness and Foreign Direct Investment in Africa,
South African Journal of Economics and Management Science, NS 9, No 3.
Kiyota, K and Shujiro, U (2004), Exchange Rate, Exchange Rate Volatility and Foreign
Direct Investment, World Economy, Blackwell Publishing Ltd, Oxford, UK.
Kyereboah-Coleman, A & Agyire-Tettey, KF (2008), Effect of Exchange-Rate Volatility on
Foreign Direct Investment in Sub-Saharan Africa: The Case of Ghana, The Journal of Risk
Finance, Vol. 9, No 1.
Leedy, PD and Ormond, JE (2005), Practical Research – Planning and Design, 8th
Edition,
Pearson Merrill Prentice Hall, New Jersey, USA.
Lemi A & Asefa S (2003), Foreign Direct Investment and Uncertainty: Empirical Evidence
from Africa, Africa Finance Journal 5.
McKenzie, MD (1999), “The impact of exchange rate volatility on international trade flows”,
Journal of Economic Surveys, Vol 13, No 1.
Mtonga, E (2006), The Real Exchange Rate of the Rand and the Competitiveness of South
Africa‟s Trade, MPRA Paper No.1192, Available from: http://mpra.ub.uni-
muenchen.de/1192/1/MPRA_paper_1192.pdf [19 July 2008].
Musila, JW and Sigué, SP (2006), Accelerating Foreign Direct Investment Flow to Africa:
From Policy Statements to Successful Strategies, Managerial Finance, Vol 32, No 7.
48
Rogoff, K (1996), The Purchasing Power Parity Puzzle, Journal of Economic Literature,
Vol 34, No 2.
Sarno, L and Taylor, MP (2002), Purchasing Power Parity and the Real Exchange Rate, IMF
Staff Papers, Vol 49, No 1.
South African Reserve Bank (1998), Quarterly Bulletin, December 1998, Pretoria, South
Africa [Online], Available from: www.reservebank.co.za [2 July 2008].
South African Reserve Bank (2001), Quarterly Bulletin, December 2001, Pretoria, South
Africa [Online], Available from: www.reservebank.co.za [2 July 2008].
South African Reserve Bank (2003), Quarterly Bulletin, December 2003, Pretoria, South
Africa [Online], Available from: www.reservebank.co.za [2 July 2008].
Standish, B (2007), Economic Data, UCT GSB Economics Module Course Data.
Takaendesa, P; Tsheole, T & Aziakpono, M (2006), Real Exchange Rate Volatility and its
Effect on Trade Flows: New Evidence from South Africa, Journal of Studies in Economics
and Econometrics, Vol. 30, No 3.
United Nations Conference on Trade and Development (2007), UNCTAD Handbook of
Statistics, Report No. TD/STAT.31 [Online]. Available from:
http://www.unctad.org/Templates/Webflyer.asp?intItemID=1397&docID=8612 [16 July
2008].
United Nations Conference on Trade and Development (2002), UNCTAD World Investment
Report, Report No. UNCTAD/WIR/2002 [Online]. Available from:
http://www.unctad.org/en/docs/wir2001overview_en.pdf [26 November 2008].
Williams, M (2006), “An Empirical Evaluation of the Factors that Influence Real Exchange
Rates: In South Africa Between 1984:1994 and 1995:2005”, MBA Thesis, University of
Cape Town, Graduate School of Business, Cape Town, South Africa
49
Xing, Y and Wan, G (2006), Exchange Rates and Competition for FDI in Asia, World
Economy, Blackwell Publishing Ltd, Oxford, UK.
9.2. WEB SITES
Beachill, B (2007), Cointegration Analysis, Available from:
www.aovh07.dsl.pipex.com/AppEcon/Cointegration%20Analysis.doc [11 July 2008]
Beachill, B (2007), Error Correction Models, Available from:
www.aovh07.dsl.pipex.com/AppEcon/Error%20Correction%20Model.doc [11 July 2008]
Beachill, B (2007), How to do unit root tests, Available from:
www.aovh07.dsl.pipex.com/AppEcon/Unit%20Root%20tests.doc [11 July 2008]
South African Reserve Bank, http://www.resbank.co.za/economics/netqb2/NetQbMain.asp
Statistics South Africa: www.statssa.gov.za
The International Monetary Fund: http://www.imfstatistics.org/imf/
9.3. BOOKS
Green, PE (1978), Analyzing Multivariate Data, Dryden Press, Hinsdale, Illinois.
Hartzenberg, T et al (2005), Economics – Fresh Perspectives, Pearson Prentice Hall, Cape
Town, South Africa.
Murray, MP (2006), Econometrics – A Modern Introduction, Pearson Education Inc,
Boston, MA.
Keller, G & Warrack, B (2003), Statistics for Management and Economics, 6th
Ed,
Thomson Brooks Cole, CA, USA.
Koop, G (2006), Introduction to Econometrics, Wiley and Sons, West Sussex.
50
Pindyck, RS & Rubinfield, DL (1998), Econometric Models and Economic Forecasts, 4th
Ed, McGraw-Hill, United States.
Stewart, KG (2005), Introduction to Applied Econometrics, Thomson Brooks Cole,
Belmont, CA, USA.
Todaro, MP (2002), Economic Development, Addison Wesley Longman, New York.
51
APPENDIX 1: UNIT ROOT TEST OUTPUTS - LEVELS
LFDIT
Null Hypothesis: LFDIT has a unit root Exogenous: None Lag Length: 0 (Automatic based on AIC, MAXLAG=12)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic 1.846468 0.9842
Test critical values: 1% level -2.586753 5% level -1.943853 10% level -1.614749 *MacKinnon (1996) one-sided p-values.
LREER
Null Hypothesis: LREER has a unit root Exogenous: None Lag Length: 3 (Automatic based on AIC, MAXLAG=12)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic -0.964615 0.2972
Test critical values: 1% level -2.587387 5% level -1.943943 10% level -1.614694 *MacKinnon (1996) one-sided p-values.
LPGDP
Null Hypothesis: LPGDP has a unit root Exogenous: None Lag Length: 12 (Automatic based on AIC, MAXLAG=12)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic 1.348895 0.9547
Test critical values: 1% level -2.589531 5% level -1.944248 10% level -1.614510 *MacKinnon (1996) one-sided p-values.
52
LFDIT_1
Null Hypothesis: LFDIT_1 has a unit root Exogenous: None Lag Length: 0 (Automatic based on AIC, MAXLAG=12)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic 1.847872 0.9842
Test critical values: 1% level -2.586960 5% level -1.943882 10% level -1.614731 *MacKinnon (1996) one-sided p-values.
LOPEN
Null Hypothesis: LOPEN has a unit root Exogenous: None Lag Length: 2 (Automatic based on AIC, MAXLAG=12)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic -1.364819 0.1591
Test critical values: 1% level -2.587387 5% level -1.943943 10% level -1.614694 *MacKinnon (1996) one-sided p-values.
LEXVOL
Null Hypothesis: LEXVOL has a unit root Exogenous: None Lag Length: 3 (Automatic based on AIC, MAXLAG=12)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic -0.277124 0.5839
Test critical values: 1% level -2.587607 5% level -1.943974 10% level -1.614676 *MacKinnon (1996) one-sided p-values.
53
APPENDIX 2: UNIT ROOT TESTS – FIRST DIFFERENCE
DLFDIT
Null Hypothesis: D(LFDIT) has a unit root Exogenous: None Lag Length: 0 (Automatic based on AIC, MAXLAG=12)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic -10.24695 0.0000
Test critical values: 1% level -2.586960 5% level -1.943882 10% level -1.614731 *MacKinnon (1996) one-sided p-values.
DLREER
Null Hypothesis: D(LREER) has a unit root Exogenous: None Lag Length: 2 (Automatic based on AIC, MAXLAG=12)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic -4.719506 0.0000
Test critical values: 1% level -2.587387 5% level -1.943943 10% level -1.614694 *MacKinnon (1996) one-sided p-values.
DLPGDP
Null Hypothesis: D(LPGDP) has a unit root Exogenous: None Lag Length: 12 (Automatic based on AIC, MAXLAG=12)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic -1.229443 0.1996
Test critical values: 1% level -2.589795 5% level -1.944286 10% level -1.614487 *MacKinnon (1996) one-sided p-values.
54
DLFDIT_1
Null Hypothesis: D(LFDIT_1) has a unit root Exogenous: None Lag Length: 0 (Automatic based on AIC, MAXLAG=12)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic -10.19804 0.0000
Test critical values: 1% level -2.587172 5% level -1.943912 10% level -1.614713 *MacKinnon (1996) one-sided p-values.
DLOPEN
Null Hypothesis: DLOPEN has a unit root Exogenous: None Lag Length: 1 (Automatic based on AIC, MAXLAG=12)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic -10.33957 0.0000
Test critical values: 1% level -2.587387 5% level -1.943943 10% level -1.614694 *MacKinnon (1996) one-sided p-values.
DLEXVOL
Null Hypothesis: DLEXVOL has a unit root Exogenous: None Lag Length: 2 (Automatic based on AIC, MAXLAG=12)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic -9.928185 0.0000
Test critical values: 1% level -2.587607 5% level -1.943974 10% level -1.614676 *MacKinnon (1996) one-sided p-values.
55
APPENDIX 3: MODEL 1 - OLS REGRESSION STEP-WISE REDUCTION (LPGDP)
INCLUDED
Dependent Variable: LFDIT Method: Least Squares Date: 11/14/08 Time: 08:23 Sample (adjusted): 1983Q2 2006Q3 Included observations: 94 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. C -40.07329 17.74763 -2.257952 0.0393
LREER -0.046103 0.374193 -0.123205 0.9036 LREER(-1) -0.643406 0.512374 -1.255735 0.2284 LREER(-2) -0.387061 0.505837 -0.765188 0.4560 LREER(-3) 0.492753 0.536813 0.917923 0.3732 LREER(-4) 0.018438 0.592593 0.031115 0.9756 LREER(-5) -0.137630 0.489185 -0.281345 0.7823 LREER(-6) -0.253036 0.473561 -0.534325 0.6009 LREER(-7) 0.112266 0.514885 0.218040 0.8303 LREER(-8) 0.232209 0.569633 0.407646 0.6893 LREER(-9) 0.176212 0.445188 0.395815 0.6978 LREER(-10) -0.708006 0.471841 -1.500519 0.1542 LREER(-11) 0.581222 0.573266 1.013878 0.3267 LREER(-12) 0.442604 0.489598 0.904014 0.3803
LOPEN -0.092042 0.413100 -0.222807 0.8267 LOPEN(-1) 0.313116 0.395672 0.791353 0.4411 LOPEN(-2) -0.567207 0.423124 -1.340522 0.2000 LOPEN(-3) 0.730898 0.440650 1.658682 0.1179 LOPEN(-4) 0.287332 0.440521 0.652255 0.5241 LOPEN(-5) 0.424145 0.465112 0.911920 0.3762 LOPEN(-6) 0.220144 0.430957 0.510824 0.6169 LOPEN(-7) 0.080220 0.375628 0.213563 0.8338 LOPEN(-8) -0.438527 0.391745 -1.119419 0.2806 LOPEN(-9) -0.087293 0.383931 -0.227366 0.8232
LOPEN(-10) 0.107352 0.436336 0.246031 0.8090 LOPEN(-11) -1.090637 0.454518 -2.399549 0.0299 LOPEN(-12) -0.507667 0.492895 -1.029969 0.3193
LEXVOL -0.132295 0.049232 -2.687153 0.0169 LEXVOL(-1) -0.135510 0.059593 -2.273947 0.0381 LEXVOL(-2) -0.031354 0.040312 -0.777781 0.4488 LEXVOL(-3) -0.033383 0.043234 -0.772142 0.4520 LEXVOL(-4) -0.049472 0.044376 -1.114836 0.2825 LEXVOL(-5) -0.024848 0.042179 -0.589110 0.5646 LEXVOL(-6) 0.038113 0.036790 1.035951 0.3166 LEXVOL(-7) 0.025895 0.038406 0.674247 0.5104 LEXVOL(-8) 0.065686 0.042494 1.545769 0.1430 LEXVOL(-9) -0.112179 0.034493 -3.252227 0.0054 LEXVOL(-10) 0.040703 0.033805 1.204061 0.2472 LEXVOL(-11) 0.001312 0.030399 0.043162 0.9661 LEXVOL(-12) 0.011016 0.027343 0.402863 0.6927
LPGDP -4.306815 2.599321 -1.656900 0.1183 LPGDP(-1) 7.321549 3.979001 1.840047 0.0856 LPGDP(-2) -4.725579 4.386663 -1.077260 0.2984 LPGDP(-3) 1.752075 4.151610 0.422023 0.6790 LPGDP(-4) 1.355458 4.039243 0.335572 0.7418 LPGDP(-5) -11.76641 4.213277 -2.792699 0.0137 LPGDP(-6) 11.85095 4.668034 2.538746 0.0227 LPGDP(-7) -4.514717 4.062705 -1.111259 0.2840 LPGDP(-8) 3.251651 3.642559 0.892683 0.3861
56
LPGDP(-9) 2.627008 3.588191 0.732126 0.4754 LPGDP(-10) -4.417631 3.448574 -1.281002 0.2196 LPGDP(-11) 8.500309 3.458606 2.457727 0.0266 LPGDP(-12) -2.757993 2.242298 -1.229985 0.2376
LFDIT_1 0.247140 0.216757 1.140172 0.2721 LFDIT_1(-1) 0.002689 0.237602 0.011319 0.9911 LFDIT_1(-2) 0.556914 0.283424 1.964948 0.0682 LFDIT_1(-3) 0.048080 0.306443 0.156896 0.8774 LFDIT_1(-4) -0.334972 0.289593 -1.156700 0.2655 LFDIT_1(-5) 0.529843 0.290085 1.826511 0.0877 LFDIT_1(-6) -0.372574 0.260449 -1.430504 0.1731 LFDIT_1(-7) 0.138453 0.280055 0.494378 0.6282 LFDIT_1(-8) -0.160408 0.269864 -0.594404 0.5611 LFDIT_1(-9) -0.247395 0.282495 -0.875752 0.3950
LFDIT_1(-10) 0.528720 0.260052 2.033133 0.0601 LFDIT_1(-11) -0.069416 0.286765 -0.242065 0.8120 LFDIT_1(-12) -0.225096 0.258796 -0.869782 0.3981
PLIN 0.192768 0.159513 1.208479 0.2456 PLIN(-1) 0.050414 0.196348 0.256757 0.8009 PLIN(-2) 0.324577 0.189627 1.711661 0.1076 PLIN(-3) -0.065695 0.168889 -0.388985 0.7028 PLIN(-4) -0.020516 0.191348 -0.107219 0.9160 PLIN(-5) 0.146735 0.194290 0.755239 0.4618 PLIN(-6) -0.004582 0.191407 -0.023938 0.9812 PLIN(-7) 0.073861 0.180254 0.409762 0.6878 PLIN(-8) -0.077270 0.210235 -0.367539 0.7184 PLIN(-9) 0.051217 0.227156 0.225470 0.8247
PLIN(-10) 0.131440 0.225625 0.582561 0.5688 PLIN(-11) -0.633841 0.219791 -2.883831 0.0114 PLIN(-12) 0.672823 0.168987 3.981519 0.0012
R-squared 0.990950 Mean dependent var 10.42855
Adjusted R-squared 0.943888 S.D. dependent var 0.308242 S.E. of regression 0.073016 Akaike info criterion -2.550663 Sum squared resid 0.079971 Schwarz criterion -0.413213 Log likelihood 198.8812 Hannan-Quinn criter. -1.687290 F-statistic 21.05637 Durbin-Watson stat 2.053761 Prob(F-statistic) 0.000000
Dependent Variable: LFDIT Method: Least Squares Date: 11/14/08 Time: 08:31 Sample (adjusted): 1983Q1 2006Q4 Included observations: 96 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. C -3.613906 3.733786 -0.967893 0.3359
LREER(-10) 0.331525 0.140676 2.356655 0.0208 LOPEN(-3) 0.422094 0.194878 2.165940 0.0332 LEXVOL -0.054088 0.020935 -2.583650 0.0115
LEXVOL(-1) -0.042335 0.020787 -2.036647 0.0449 LEXVOL(-9) -0.004411 0.019370 -0.227730 0.8204
LPGDP -0.066597 0.940838 -0.070784 0.9437 LPGDP(-1) 1.634291 1.157917 1.411406 0.1619 LPGDP(-5) -1.028294 0.626106 -1.642364 0.1043 LFDIT_1(-2) 0.759845 0.098895 7.683362 0.0000 LFDIT_1(-5) -0.084823 0.116501 -0.728088 0.4686
57
LFDIT_1(-10) -0.026214 0.098809 -0.265305 0.7914 PLIN(-2) 0.009720 0.064252 0.151282 0.8801
PLIN(-12) 0.103824 0.056473 1.838474 0.0696 R-squared 0.902870 Mean dependent var 10.42704
Adjusted R-squared 0.887471 S.D. dependent var 0.319077 S.E. of regression 0.107035 Akaike info criterion -1.497277 Sum squared resid 0.939439 Schwarz criterion -1.123310 Log likelihood 85.86930 Hannan-Quinn criter. -1.346113 F-statistic 58.63279 Durbin-Watson stat 0.963629 Prob(F-statistic) 0.000000
Dependent Variable: LFDIT Method: Least Squares Date: 11/14/08 Time: 08:34 Sample (adjusted): 1983Q1 2006Q4 Included observations: 96 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. C -4.956216 3.143641 -1.576584 0.1184
LREER(-10) 0.310892 0.081010 3.837719 0.0002 LOPEN(-3) 0.635332 0.132771 4.785181 0.0000 LEXVOL -0.073015 0.020087 -3.635000 0.0005
LPGDP(-5) 0.687999 0.271625 2.532906 0.0131 LFDIT_1(-2) 0.678681 0.071592 9.479839 0.0000 PLIN(-12) 0.067524 0.054363 1.242086 0.2175
R-squared 0.884705 Mean dependent var 10.42704
Adjusted R-squared 0.876932 S.D. dependent var 0.319077 S.E. of regression 0.111935 Akaike info criterion -1.471668 Sum squared resid 1.115128 Schwarz criterion -1.284684 Log likelihood 77.64007 Hannan-Quinn criter. -1.396086 F-statistic 113.8219 Durbin-Watson stat 0.869862 Prob(F-statistic) 0.000000
58
APPENDIX 4: MODEL 2 - OLS REGRESSION STEP-WISE REDUCTION (LPGDP)
EXCLUDED
Dependent Variable: LFDIT Method: Least Squares Date: 11/14/08 Time: 08:47 Sample (adjusted): 1983Q2 2006Q3 Included observations: 94 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. C 5.027284 5.893842 0.852972 0.4009
LREER -0.202275 0.326619 -0.619300 0.5407 LREER(-1) 0.131399 0.423370 0.310365 0.7586 LREER(-2) -0.333154 0.415537 -0.801742 0.4295 LREER(-3) 0.031952 0.464870 0.068732 0.9457 LREER(-4) 0.360379 0.468237 0.769649 0.4480 LREER(-5) 0.530954 0.457174 1.161382 0.2553 LREER(-6) -0.534107 0.428810 -1.245558 0.2232 LREER(-7) -0.449848 0.457690 -0.982866 0.3341 LREER(-8) 0.170472 0.479594 0.355451 0.7249 LREER(-9) 0.208520 0.407344 0.511901 0.6127 LREER(-10) 0.066785 0.413208 0.161626 0.8728 LREER(-11) -0.449108 0.410546 -1.093930 0.2833 LREER(-12) 0.350674 0.257761 1.360463 0.1845
LOPEN -0.041499 0.358079 -0.115894 0.9086 LOPEN(-1) 0.248631 0.360903 0.688914 0.4965 LOPEN(-2) 0.154440 0.381348 0.404985 0.6886 LOPEN(-3) 0.393695 0.391341 1.006016 0.3230 LOPEN(-4) -0.334763 0.401927 -0.832894 0.4120 LOPEN(-5) -0.191488 0.417605 -0.458537 0.6501 LOPEN(-6) 0.527875 0.401458 1.314894 0.1992 LOPEN(-7) -0.074727 0.389677 -0.191768 0.8493 LOPEN(-8) -0.134439 0.386831 -0.347539 0.7308 LOPEN(-9) -0.114787 0.373089 -0.307666 0.7606
LOPEN(-10) 0.578571 0.379308 1.525331 0.1384 LOPEN(-11) 0.021928 0.351844 0.062322 0.9507 LOPEN(-12) -0.202579 0.317139 -0.638771 0.5282
LEXVOL -0.029569 0.035067 -0.843212 0.4063 LEXVOL(-1) -0.010892 0.036682 -0.296923 0.7687 LEXVOL(-2) 0.004341 0.032634 0.133034 0.8951 LEXVOL(-3) 0.012572 0.030454 0.412807 0.6829 LEXVOL(-4) 0.080928 0.030234 2.676711 0.0123 LEXVOL(-5) -0.018994 0.033375 -0.569115 0.5738 LEXVOL(-6) -0.001019 0.034778 -0.029297 0.9768 LEXVOL(-7) -0.003178 0.033381 -0.095197 0.9248 LEXVOL(-8) 0.020478 0.031318 0.653876 0.5185 LEXVOL(-9) -0.050543 0.029908 -1.689935 0.1021 LEXVOL(-10) -0.004167 0.027814 -0.149818 0.8820 LEXVOL(-11) 0.024446 0.026994 0.905588 0.3729 LEXVOL(-12) 0.027798 0.025260 1.100476 0.2805
LFDIT_1 0.665213 0.169886 3.915652 0.0005 LFDIT_1(-1) 0.178546 0.212216 0.841339 0.4073 LFDIT_1(-2) 0.141541 0.219846 0.643817 0.5249 LFDIT_1(-3) 0.144954 0.228936 0.633162 0.5318 LFDIT_1(-4) -0.259386 0.233213 -1.112229 0.2755 LFDIT_1(-5) 0.100082 0.238756 0.419181 0.6783 LFDIT_1(-6) -0.230030 0.244648 -0.940252 0.3551 LFDIT_1(-7) 0.142494 0.271719 0.524416 0.6041 LFDIT_1(-8) -0.288232 0.247175 -1.166102 0.2534
59
LFDIT_1(-9) 0.030510 0.255087 0.119607 0.9056 LFDIT_1(-10) 0.229476 0.262550 0.874028 0.3895 LFDIT_1(-11) -0.120190 0.285269 -0.421323 0.6767 LFDIT_1(-12) -0.071304 0.233436 -0.305455 0.7623
PLIN -0.140835 0.121574 -1.158431 0.2565 PLIN(-1) 0.136587 0.154700 0.882914 0.3848 PLIN(-2) -0.049737 0.157130 -0.316534 0.7539 PLIN(-3) -0.046485 0.161163 -0.288436 0.7751 PLIN(-4) -0.068753 0.171621 -0.400611 0.6917 PLIN(-5) 0.067898 0.182813 0.371409 0.7131 PLIN(-6) 0.036460 0.183210 0.199008 0.8437 PLIN(-7) -0.053507 0.174209 -0.307143 0.7610 PLIN(-8) -0.042906 0.180867 -0.237225 0.8142 PLIN(-9) 0.112300 0.190288 0.590159 0.5598
PLIN(-10) -0.120830 0.203785 -0.592929 0.5580 PLIN(-11) -0.297772 0.210247 -1.416298 0.1677 PLIN(-12) 0.344166 0.145704 2.362093 0.0254
R-squared 0.977092 Mean dependent var 10.42855
Adjusted R-squared 0.923913 S.D. dependent var 0.308242 S.E. of regression 0.085025 Akaike info criterion -1.898580 Sum squared resid 0.202419 Schwarz criterion -0.112862 Log likelihood 155.2333 Hannan-Quinn criter. -1.177281 F-statistic 18.37365 Durbin-Watson stat 2.064527 Prob(F-statistic) 0.000000
Dependent Variable: LFDIT Method: Least Squares Date: 11/14/08 Time: 09:03 Sample (adjusted): 1983Q1 2006Q4 Included observations: 96 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. C 1.540691 0.505959 3.045093 0.0030
LOPEN(-10) 0.165782 0.117474 1.411221 0.1616 LEXVOL(-9) -0.021875 0.013228 -1.653737 0.1016
LFDIT_1 0.851676 0.045469 18.73085 0.0000 PLIN(-12) 0.026218 0.044716 0.586318 0.5591
R-squared 0.938994 Mean dependent var 10.42704
Adjusted R-squared 0.936313 S.D. dependent var 0.319077 S.E. of regression 0.080523 Akaike info criterion -2.149861 Sum squared resid 0.590045 Schwarz criterion -2.016301 Log likelihood 108.1933 Hannan-Quinn criter. -2.095874 F-statistic 350.1650 Durbin-Watson stat 1.949784 Prob(F-statistic) 0.000000