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O R I G I N A L P A P E R

Determining the asymmetric effects of oil price changes

on macroeconomic variables: a case study of Turkey

Yeliz Yalcin • Cengiz Arikan • Furkan Emirmahmutoglu

Published online: 18 October 2014 Springer Science+Business Media New York 2014

Abstract This paper aims to investigate the effects of unanticipated oil price

changes on the Turkish economy using quarterly gross domestic product (GDP) and

monthly consumer price index (CPI) and real exchange rate (RER) for the period

2002–2013. While the bulk of previous studies have employed the standard meth-

odology without true data generating process knowledge, in this study asymmetric

Vector Autoregressive methodology proposed by Kilian and Vigfusson (Quant Econ

2(3): 419–453, 2011) is used to analyze the asymmetric impact of oil prices onmacroeconomic aggregates. This method allows the researcher to investigate the

asymmetric effects of innovations in oil prices on variables without knowing data

generating process is linear or not. Empirical findings that, the oil prices changes

have asymmetric effects on CPI and RER at one standard deviation shocks in

different periods unlike GDP. These asymmetric effects are also statistically sig-

nificant at 10 % significance level. Specifically, when oil price increases, CPI and

RER increases but GDP decreases in the long term.

Keywords Oil price Turkish economy Asymmetric effect VAR

Y. Yalcin C. Arikan F. Emirmahmutoglu (&)Department of Econometrics, Gazi University, 06500 Ankara, Turkey

e-mail: [email protected]

URL: http://websitem.gazi.edu.tr/furkan

Y. Yalcin


URL: http://websitem.gazi.edu.tr/yyeliz

C. Arikan


C. Arikan

Turkish Ministry of Customs and Trade, Ankara, Turkey

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Empirica (2015) 42:737–746

DOI 10.1007/s10663-014-9274-y

http://crossmark.crossref.org/dialog/?doi=10.1007/s10663-014-9274-y&domain=pdfhttp://crossmark.crossref.org/dialog/?doi=10.1007/s10663-014-9274-y&domain=pdf


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JEL Classification C01 C32 C34 C82

1 Introduction

The effects of changes in oil prices on economic activity receive a considerable

amount of attention by economists and policymakers. Most of the studies assume

that the relationship between oil prices and macroeconomic aggregates is linear and

therefore, estimate it by using standard linear Vector Autoregressive (VAR) model

and cointegration framework (for example Hamilton 1983; Gisser and Goodwin

1986; Bohi 1991 etc.). However, it has been found that the decreases in the oil

prices that take place after the second half of 1980s have smaller positive effects on

economic activity than predicted by usual linear models (Lardic and Mignon 2008).Mork (1989) is the first study to provide the empirical evidence on asymmetric

effects1 of oil price shocks on output. After this influential work, the asymmetric

relationship between oil prices and economic aggregates has begun to gain

importance (for example Mork 1989, 1994; Mory 1993; Hamilton 1996, 2003;

Brown and Yücel 2002; Mehrara 2008; Kilian and Vigfusson 2011).

There is a vast literature to investigate different channels of the asymmetric effect

of oil prices on the macroeconomics variables. First, adjustment costs suggested by

Hamilton (1988) could lead to an asymmetric response to changing oil prices.

Rising (falling) oil prices hinder (stimulate) the economic activities directly.

However, the costs of adjusting to changing oil prices also slow down the economic

activities. Rising oil prices present two negative effects for the economic activities.

Falling oil prices present both negative and positive effects, which would tend to be

offsetting. (Brown and Yücel 2002). Second, the study by Ferderer (1996)

emphasizes that if oil price volatility has an adverse impact on the economic activity

and volatility also rises due to increases and decreases in oil price shock, then

uncertainty has the potential to explain asymmetry. Third, asymmetry may stems

from a monetary policy. The study by Bernanke et al. (1997) indicates that the

Federal Reserve Bank responds more aggressively to the rises in crude oil prices

than it does to falls in the U.S. economy (Herrera et al. 2014).

If the true relation is linear and one mistakenly estimates a nonlinear

specification, the resulting estimates are asymptotically biased (Kilian and

Vigfusson 2011). Moreover, if the true relation is nonlinear and one mistakenly

estimates a linear specification, the resulting estimates are asymptotically biased

(Hamilton 2003). In order to avoid either problem, Kilian and Vigfusson (2011)

suggest a new approach, which consists of including both linear and nonlinear

terms. The biggest advantage of their method is that the impulse responses are

consistent regardless of whether the data generating process is symmetric or

asymmetric. In other words, this method can be used without knowing the nature of the true data generating process (DGP).

1 The asymmetric effect can be defined as increases and decreases in any variable do not have same

effect on any variable or economy.

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In this study in order to examine the asymmetric impacts of oil price fluctuations

on economic activity in Turkey, Kilian and Vigfusson’s (2011) asymmetric VAR

methodology is used. There are several underlying reasons why the Turkish

economy might be an important case study to examine the relationship between oil

prices and macroeconomic aggregates. First, since Turkey can only produce 10 %of its oil demand; it is an important oil importing country when compared to other

OECD countries. Second, oil expenditure has a big part in GDP, as well as the

balance of international payment. Therefore, the changes in oil prices affect the

Turkish economy drastically. Third, in Turkey the biggest portion of special

consumption tax revenues comes from oil and natural gas so the changes in oil price

and exchange rates are crucial for policy makers (Alper and Torul 2010; Berument

et al. 2010).

Empirical evidence regarding the relationship between oil price and macroeco-

nomic aggregates for Turkey is provided by a number of studies that employalternative estimation methods, i.e. different macroeconomic variables and for

different time periods. Even though most of these studies focus on the symmetric

relationship between oil prices and macroeconomic variables,2 the studies on the

asymmetric effects of oil price fluctuations on the Turkish economy are rather

limited. Alper and Torul (2010) examine the asymmetric effects of oil prices on the

manufacturing sector for the period 1990–2007, using VAR models. Their empirical

findings suggest that oil price increases affect several manufacturing sectors

asymmetrically, i.e. wood, furniture, chemical. Catik and Onder (2013)analyze the

asymmetric effects of oil prices on the economic activity for Turkey by using amultivariate two-regime Threshold VAR model. Their results suggest that the

relationship between oil price changes and economic activity is nonlinear and shows

an asymmetric pattern.

The purpose of this study to investigate the asymmetric effects of oil price

changes on the macroeconomic aggregates including real gross domestic products,

consumer price index and exchange rate; by employing Kilian and Vigfusson’s

(2011) model. To this end, monthly and quarterly data are utilized for the Turkish

economy covering the period 2002–2013. This paper proceeds as follows.

Sections 2 and 3 presents methodology, and the data set and estimation results,

respectively. Conclusions are given in Sect. 4.

2 Methodology

In this study, Kilian and Vigfusson’s (2011) methodology is employed to examine

the relationship between oil price volatility and macroeconomic aggregates for

Turkey, since their model can be applied without knowing the nature of the DGP.

The VAR ( p) model in Kilian and Vigfusson (2011) is:

2 There is a literature on the symmetric impacts of oil prices on macroeconomic aggregates for Turkey.

See, for example, Alper and Torul 2008; Berument and Taşçı 2002; Diboglu and Kibritcioglu 2003;

Kibritcioglu 2003; Özlale and Pekkurnaz 2010; Yaylali and Lebe 2012, Gökçe 2013).

Empirica (2015) 42:737–746 739

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X t ¼ a10 þX pi¼1

a1iY t i þX pi¼1

a2i X t i þ e1t

Y t ¼ b

10þX p

i¼1

b1iY t i

þX pi¼1

b2i X

t iþX p

i¼0

g2i X þ

t iþ e

2t

ð1Þ

where the X t and Y t is the percentage in oil price and the macroeconomic variable,

respectively, and et WN 0;Rð Þ. While the first equation is standard linear VAR, thesecond equation includes both oil price increase and decrease effects by X t and X

þt .

Here X þt is a censored variable and can be defined as fallows

X þt ¼ X t ; X t [ threshold value

0; X t threshold value

( ð2Þ

where threshold value can be estimated by using Chan (1993) or can be taken as

zero. Equation (1) can be estimated by standard regression since the OLS residuals

of model (1) are uncorrelated (Kilian and Vigfusson 2011).

In order to test whether there is an asymmetric effect, Kilian and Vigfusson

(2011) has suggested two different methods:

2.1 Slope based test

The slope based test does not require the calculation of an impulse response and any

other properties. This test is based on statistical testing the censored variables in

model (1). The null hypothesis for the test:

H 0 : g21;0 ¼ ¼ g21; p ¼ 0

This test has an asymptotic v2 pþ1 distribution. Although slope based tests are

useful to determine the asymmetry, they do not give any idea about the direction and

level of the asymmetry.

2.2 Impulse response based tests

Generalized impulse response functions to both positive and negative oil price

shocks by using model (1) are calculated. The null hypothesis of test:

H 0 : I yðh; dÞ ¼ I yðh; dÞ

where I y h; dð Þ and I y h; - dð Þ are responses of Y t at horizon h ¼ 1; 2; . . .; H to a

shock of d. This test has an asymptotic v2 H þ1 distribution. Against the slope based

test, this test depends on the magnitude of shock. Therefore the impulse response

based test more relative and powerful than the slope based test (Kilian and Vig-fusson 2009).

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3 Data and empirical results

In this study, quarterly data on the real gross domestic product (GDP), monthly data

for the consumer price index (CPI), the real effective exchange rate (RER) and the

brent oil price are used. The data cover the period 2002:01-2013:12.3

Since theseries have seasonal pattern, they are adjusted using the Tramo/Seats method. All

variables are used in logarithm form. The time series properties of all the variables

included in this study are examined using Augmented Dickey-Fuller (ADF, 1981)

and Phillips Perron (PP, 1988) unit root tests. The null hypothesis of the ADF and

PP tests for both the model with constant term and the one with a constant and trend

term is that the time series contains a unit root. The results of these tests are given in

Table 1.

According to unit root tests results, unit root null cannot be rejected at

conventional significance levels, at the 1 % significance level, for all variables.Therefore, the first differences of all the variables are used when constructing the

VAR. After the censored variable is defined by replacing negative values of changes

in oil prices with zero, model (1) has been built-up for each macro-economic

variable and oil price in first differences, separately.4 Since Turkey has no impact on

oil price determination, lags of Y t are excluded from the first equation in model (1).

So the model becomes:

X t ¼ a10 þX p

i¼1

a2i X t i þ e1t

Y t ¼ b10 þX pi¼1

b1iY t i þX pi¼1

b2i X t i þX pi¼0

g2i X þt i þ e2t

ð3Þ

In order to explore the effect of the positive oil price shocks on each macro-

economic aggregate, we calculated the generalized impulse responses. As discussed

in Koop et al. (1996) and Potter (2000), multivariate nonlinear models produce

impulse responses, which are history and shock dependent. Since the model (1) is

nonlinear, the generalized impulse responses are calculated using the procedure

given in Kilian and Vigfusson (2011). The procedure can be outlines as follows:

1. Xi information set, include all lagged values of X t and Y t , is defined.

2. Given Xi two time path X t þh and Y t þh are generated. When generating the first

time path, e1t value is set equal to a predetermined value d. The realizations of

e1;t þhðh ¼ 1; 2; . . .; H Þ are drawn from the marginal empirical distribution of e1t .The realizations of e2;t þhðh ¼ 0; 1; . . .; H Þ are drawn independently from themarginal distribution of e2t . When generating the second time path, all e1;t þh and

e2;t þhðh ¼ 0; 1; . . .; H Þ are drawn from their respective marginal distributions.

3 The data of brent oil price, gross domestic product and consumer price, real effective exchange rate are

obtained from International Energy Agency: http://www.iea.org., The Central Bank of the Republic of

Turkey: http://evds.tcmb.gov.tr/ , respectively.4 Akaike Information Criteria (AIC) is used to determine the optimal lag lengths of the model; the lag

order was 6 for GDP, 2 for CPI and 6 for RER.

Empirica (2015) 42:737–746 741

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3. Calculate the difference between the time paths for Y t þhðh ¼ 0; 1; . . .; H Þ4. Average this difference across m = 500 repetitions of Steps 2 and 3 (Kilian and

Vigfusson 2011)

The impulse responses of each macro-economic aggregate when one standard

deviation positive shock is given to oil price are plotted out in the left panel of

Fig. 1. The dotted lines show the 95 % confidence intervals5

based on the

bootstrap simulation with 500 trials are calculated for responses to a positive

shock. The history dependent impulse responses are reported for 6 periods6 for

GDP and 12 periods for CPI and RER. In linear models, the impulse response to a

positive shock is by construction the mirror image of the response to a negative

shock of the same type and size (Hove 2012). Therefore, if there is an asymmetry,

positive and negative shocks are not mirror images of one another (Balke et al.

2002). To compare the positive shock and negative shock impulse responses, boththe responses to positive and the responses to negative shocks are given together

in the right panel of Fig. 1. The dotted line shows responses to a negative shock

and the straight line shows the responses to positive shock from the asymmetric

model.

The right panel of Fig. 1 reports the responses to a positive shock, I y h; dð Þ,and theresponses to a negative shock, I y h; dð Þ for GDP, CPI and RER. Although theresponses of GDP to a negative shock are nearly invisible, there are the small

absolute distance between the responses to a positive and a negative shock of the

same magnitude for CPI and RER. These results may provide an evidence for the

Table 1 The ADF and PP unit root test results

GDP CPI RER Oil price

ADF

Level

Constant -0.799 0.090 -2.218 -2.078

Constant and trend -1.860 -1.943 -2.119 -2.339

First difference

Constant -5.158*** -7.967*** -8.285*** -6.435***

PP

Level

Constant -0.832 0.106 -2.343 -1.887

Constant and trend -2.190 -1.780 -2.351 -3.118

First difference

Constant -5.158*** -7.959*** -7.806*** -8.039***

*,**,*** Statistically significant at the 10, 5, 1 % level, respectively

5 We also consider at 90 % confidence levels. However, our results are almost same at both confidence

levels.6 Since the data of GDP is quarterly, the periods of impulse responses are taken for 1.5 year.

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existence of asymmetry for inflation and exchange rate. In order to test whether this

distance is statistically significant, in other words, whether there is an asymmetric

effect of oil price changes on GDP, CPI and RER, impulse response based tests are

used. Since the impulse response based test is more powerful, it is preferred in this

study. The p-values of the test H 0 : I y h; dð Þ ¼ I y h; dð Þ for 8 periods are given inTable 2.

When a one standard deviation shock is considered, the symmetry null

hypothesis cannot be rejected at 5 % significance level for all periods for GDP.

That is, there is no evidence against the symmetry null hypothesis for GDP inresponse to a 1 standard deviation shock except the first period at 10 % significance

level. However, at all periods the null hypothesis can be rejected at 5 % significance

level for CPI and exchange rate. In other words, the impacts of oil price changes on

CPI and RER are asymmetric.

Fig. 1 Effects of oil price shock, asymmetric model

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According to the left panel of Fig. 1, although the changes in oil prices increase the

GDP growth, the cumulative effects of GDP decrease with the total effect of -0.3419

for 12 periods. This result is similar to the findings of Jones et al. (2004) and Lardic and

Mignon (2008). The impacts of oil price shock on CPI and RER are positive, which is

parallel to the literature (see Leblanc and Chinn 2004). According to the right panel of

Fig. 1, the oil price increases have larger impact on both GDP growth and inflation than

the oil price declines. However, the negative oil price shocks have larger effect on RER

than the positive oil price shocks. There are many reasons supporting these results for

Turkey as an oil importer country. Since oil is a necessary good for Turkey and the share

of oil in the budget is large, the negative oil price shocks have bigger impact on the

exchange rate. Also, the taxes imposed by the government on oil prices increased the

pump prices. This increase has further increased the exchange rate.

4 Conclusions

The aim of this paper is to estimate the asymmetric effects of oil price changes on

the macroeconomic variables for a small open economy like Turkey. In order to

investigate the asymmetric impact of oil prices, this study uses an asymmetric VAR

model that is proposed by Kilian and Vigfusson (2011) employing quarterly GDP,

and monthly CPI and RER data for the period 2002–2013. There is a wide

consensus that the effect of oil prices on macroeconomic aggregates is asymmetric.

Unlike the existing literature where the standard methodology is used without

knowing the true DGP, in this study the asymmetric impact of oil prices is

investigated utilizing an asymmetric VAR model. Empirical evidence from the

impulse responses suggests that although the impact of oil price changes on GDP is

symmetric after the first quarter, CPI and RER are affected asymmetrically for allperiods. The positive oil price shock has a smaller positive effect on CPI but larger

positive effect on RER. The GDP is also at first positively affected from a positive

oil price shock, but the overall cumulative effect is negative for 12 periods used in

this study.

Table 2 The impulse response based tests results

Period GDP CPI RER

1 0.0903* 0.0000*** 0.0007***

2 0.2303 0.0000*** 0.0030***

3 0.2382 0.0000*** 0.0024***

4 0.2833 0.0001*** 0.0059***

5 0.2906 0.0002*** 0.0119**

6 0.4014 0.0004*** 0.0230**

7 0.4813 0.0002*** 0.0401**

8 0.5784 0.0005*** 0.0635*

p Values are based on the v2 H þ1 distribution

*, **, *** Statistically significant at the 10, 5, 1 % level, respectively

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