International Review of Economics and Finance 17 (2008) 380–387www.elsevier.com/locate/iref

The Feldstein–Horioka puzzle across EU members:Evidence from the ARDL bounds approach and panel data

Christos Kollias a,⁎, Nikolaos Mylonidis b, Suzanna-Maria Paleologou b

a Department of Economics, University of Thessaly, Argonafton and Filellinon, Volos 38221, Greeceb Department of Economics, University of Ioannina, Greece

Received 22 February 2006; received in revised form 5 July 2006; accepted 27 September 2006Available online 6 December 2006

Abstract

This paper addresses the saving-investment (SI) correlation for the EU15 member countries, using the ARDL approach and panelregressions. If we accept the Feldstein–Horioka [Feldstein,M. and C. Horioka, 1980, Domestic saving and international capital flows,Economic Journal 90, 314–329.] interpretation of the SI correlation, the evidence from the ARDL approach does not point to anyparticular direction in terms of country size, or level of development, or economic and capital market structure. Panel regressions yieldan SI coefficient in the range of 0.148–0.157. This finding is attributed to higher capital mobility, lower transaction costs in theinternational capital markets, and the declining status of long-run current account targeting as a primary government objective.© 2006 Elsevier Inc. All rights reserved.

JEL classification: C22; F21; F32; F41; E21Keywords: Feldstein–Horioka puzzle; Saving-investment; Cointegration; Panel; European Union

1. Introduction

One of the frequently reported stylized facts in modern open economies is the high and positive correlationbetween national savings and domestic investment. According to the seminal contribution by Feldstein and Horioka(1980) (henceforth FH) that has sparked off a large and growing body of literature and intense debate, this contradictsthe general consensus of increasing international capital mobility especially among developed countries. Theliterature on what has become known as the FH puzzle has rapidly grown and by now there exists an abundance ofrelevant work, an extensive and critical survey of which can be found in Frankel (1992) and in Coakley, Kulasi, andSmith (1998).

The extensive empirical literature on the FH issue varies significantly in terms of the methodology employed, aswell as the data set and sample periods covered. In general, the FH result has been mainly replicated using cross-sectionregressions (see inter alia: Artis & Bayoumi, 1992; Dooley, Frankel, & Mathieson, 1987; Feldstein, 1983; Feldstein &Bachetta, 1991; Murphy, 1984; Obstfeld, 1995; Penati & Dooley, 1984; Tesar, 1991;), and, at a lesser extent, panelestimation techniques (see inter alia: Coakley & Kulasi, 1997; Coakley, Kulasi & Smith, 1996; Corbin, 2001; Feldstein,1983; Ho, 2002; Jansen, 2000; Kim, 2001; Krol, 1996). Time-series analysis has provided a wider dispersion of saving-

⁎ Corresponding author.E-mail address: kollias@econ.uth.gr (C. Kollias).

1059-0560/$ - see front matter © 2006 Elsevier Inc. All rights reserved.doi:10.1016/j.iref.2006.09.002

381C. Kollias et al. / International Review of Economics and Finance 17 (2008) 380–387

investment (SI) coefficients (see inter alia: Alexakis & Apergis, 1994; Apergis & Tsoulfidis, 1997; Bajo-Rubio, 1998;Caporale, Panopoulou, & Pittis, 2005De Vita & Abbott, 2002; Obstfeld, 1986; Pelagidis &Mastroyiannis, 2003; Sinha& Sinha, 1998, 2004; Tesar, 1993).

The interpretation of the SI association has sparked a large and growing body of literature and debate. Thewidespread view that the SI coefficient is simply associated with the degree of capital mobility (Dooley et al., 1987;Feldstein, 1983; Penati & Dooley, 1984; Sinha & Sinha, 2004; Vos, 1988) has been heavily criticized.1 Alternativeinterpretations are commonly found in the literature. The first is the long-run current account targeting which is likely toproduce a high SI coefficient and, most notably, the intertemporal budget constraint which implies that saving andinvestment are cointegrated with a unit coefficient regardless of the degree of capital mobility (Artis & Bayoumi, 1992;Banerjee & Zanghieri, 2003; Coakley & Kulasi, 1997; Coakley et al., 1996; Jansen, 1996, 1998; Sachs, 1981; Sinha &Sinha 1998, 2004). It should be mentioned, however, that this interpretation may justify a unity SI coefficient, but itdoes not mean that the reverse is true, i.e., a unity SI coefficient does not necessarily imply sustainability of currentaccount (Sachsida & Caetano, 2000). The second approach to interpret the SI coefficient is related to the country size.Harberger (1980) argues that the FH result simply reflects the fact that a large country is more reliant on domesticsources of financing. Consistent with this interpretation, Murphy (1984), Baxter and Crucini (1993) and Mamingi(1994)) find that smaller OECD or developing countries exhibit higher capital mobility than larger ones. This finding isattributed to the fact that smaller countries cannot influence world interest rates, and thus their SI correlation is notbiased upwards. The presence of temporary business cycle shocks, e.g., in productivity, is another plausibleexplanation of a persistent SI correlation (Baxter & Crucini, 1993; Mendoza, 1991; Obstfeld, 1986). The fourthinterpretation is related to the existence of a home bias due to the association of high costs with foreign markets whichreduce the international diversification of portfolios (Georgopoulos & Hejazi, 2005; Gordon & Bovenberg, 1996;Hericourt & Maurel, 2005). The fifth interpretation suggests that the SI correlation coefficient reflects thesubstitutability between domestic and external savings (Sachsida & Caetano, 2000). The last interpretation represents aspecial case and refers to highly integrated regions and currency unions, such as the EU. Blanchard and Giavazzi(2002) argue that a weaker SI association may simply reflect higher financial and trade integration.

Hoping to contribute to the existing pool of evidence, the present studymakes an empirical contribution to the literatureon the FH puzzle.More specifically, it employs the bounds testing procedure to the analysis of level relationships within anautoregressive distributed lag (ARDL) framework (Pesaran, Shin,& Smith, 2001). The usefulness of this approach is that itallows testing for the presence of cointegration when it is not known with certainty whether the regressors are purely I(0),purely I(1), or mutually cointegrated. This methodology seems particularly appropriate in the context of the FH puzzle,where the investment ratio is often found to be I(1), but there is uncertainty as to the order of integration of the savings ratio.In addition, the panel structure of the data is investigated to account for possible cross sectional and time perioddependence. This issue seems particularly relevant for the FH debate where a very parsimonious regressionmodel is used.In such a context, it is quite likely that the model omits common factors (e.g., individual country specific effects andbusiness cycle shocks) that are potentially correlated with the explanatory variable.

The sample comprises EU15 member countries over the period 1962–2002. In many respects, this group ofcountries represents an interesting sample for empirical investigation. For instance, to the extent that country sizeinfluences the degree of capital mobility, the EU15 includes both small and large economies with different levels ofdevelopment. Furthermore, they exhibit different economic structures, different degrees of integration in theinternational economy (e.g. Luxemburg vs. Greece), and different growth performances over the years (e.g., Ireland vs.Germany), and thus different profit opportunities for international capital.

2. Empirical analysis

Essentially, investigating the SI relationship entails the estimation of the following regression:

IGDP

� �i

¼ aþ bS

GDP

� �i

þui ð1Þ

1 Alternative measures of capital mobility have been suggested in the literature. For example, Shibata and Shintani (1998) propose the use ofconsumption and net output for this purpose.

382 C. Kollias et al. / International Review of Economics and Finance 17 (2008) 380–387

where β is the SI retention coefficient and ui is the standard error. As Feldstein and Horioka (1980) point out, Eq. (1)allows for the investigation of the capital mobility hypothesis. Specifically, under the perfect capital mobilityhypothesis there should be no relation between domestic saving and investment. In such a case β is expected to bearound zero. This would suggest that saving in each country moves globally responding to worldwide opportunities forinvestment seeking higher profitability. At the same time, domestic investment in the given country will be financed bythe worldwide pool of capital (Feldstein & Horioka, 1980; p. 317). However, in light of the literature reviewed in theintroduction, the SI relationship may be more complex than that purported by Feldstein and Horioka. It is now likelysaving and investment shares of GDP to be significantly correlated regardless of the degree of financial integration. Forexample, following Coakley et al. (1996), the cointegration between these two variables is a necessary condition forcurrent account solvency.

To test these hypotheses within the FH framework, we use annual data (1962–2002) for the EU15 membercountries. We follow Sinha (2002) and take gross domestic saving as a measure of saving. In line with previousliterature, saving is defined simply as GDP minus both private and government consumption. Baxter and Crucini(1993) call this measure basic saving. For a measure of investment gross fixed capital formation is used. To allow forhomogeneity, all data used herein are drawn from Eurostat.

2.1. The ARDL bounds testing procedure

Cointegration techniques employed by previous studies require that all the system's variables are integrated of thesame order. As De Vita and Abbott (2002) point out, there often exists substantial uncertainty over the time seriesproperties of the two variables involved in studying the FH puzzle. Indeed, as it can be seen in Table 1 this uncertaintyalso appears to be the case in the sample of countries used here. The reported results illustrate the uncertainty overwhether the ratio of gross fixed capital formation to GDP, as a measure of investment and the ratio of gross nationalsaving to GDP for the EU15 member states are I(0) or I(1). While the Phillips–Perron (PP) tests, and at a lesser extentthe Augmented Dickey–Fuller (ADF) tests, suggest that the null hypothesis that a series is I(1) cannot be rejected, theKPSS test results indicate that, in most instances, the null hypothesis that the same series is I(0) is accepted at the 1%significance level, or better. Overall, what emerges from Table 1 is the ambiguous nature of the dynamic properties ofthe series under consideration, with a mixture of I(1) and I(0) series spanning the data set.

Table 1Unit root tests

Countries I/GDP S/GDP

ADF PP KPSS ADF PP KPSS

Austria −1.344 −1.340 0.535⁎ −1.319 −1.476 0.603⁎

Belgium −1.395 −1.348 0.498⁎ −1.390 −1.455 0.286Denmark −1.426 −1.533 0.572⁎ −2.167 −1.676 0.173Finland −2.512 −1.447 0.529⁎ −1.613 −2.541 0.148France −2.081 −0.923 0.656⁎ −1.522 −1.182 0.570⁎

Germany −1.909 −1.626 0.490⁎ −3.250⁎ −1.578 0.551⁎

Greece −1.288 −2.095 0.348⁎⁎ −1.032 −1.661 0.436⁎⁎

Ireland −2.027 −1.913 0.160 −1.294 −1.031 0.712⁎

Italy −1.058 −1.690 0.688⁎ −1.850 −1.920 0.742⁎⁎⁎

Luxembourg −3.148⁎ −2.911⁎⁎ 0.314 −1.495 −1.418 0.264Netherlands −1.232 −1.295 0.567⁎ −1.892 −1.892 0.160Portugal −3.361⁎ −2.474 0.131 −3.909⁎⁎⁎ −2.459 0.268Spain −2.945⁎ −1.697 0.272 −3.898⁎⁎⁎ −3.929⁎⁎⁎ 0.554⁎

Sweden −1.662 −1.158 0.668⁎ −2.257 −1.880 0.376⁎⁎

UK −2.369 −1.717 0.468⁎ −0.932 −1.024 0.721⁎

Data source: Eurostat as published in The Greek Economy in Figures 2003, pp. 24–73.Notes: regressions are estimated with an intercept but no trend. The critical values for ADF at 10%, 5% and 1% significance levels are - 2.606, - 2.936and - 3.605, respectively; for those for PP are - 2.607, - 2.938 and - 3.610, respectively; for those for KPSS are 0.347, 0.463 and 0.739, respectively.The lag order of ADF unit root test is based on Schwarz Bayesian Criterion. ⁎ denotes the rejection of the null at the 5% significance level; ⁎⁎ denotesthe rejection of the null at the 10% significance level; ⁎⁎⁎ denotes the rejection of the null at the 1% significance level.

383C. Kollias et al. / International Review of Economics and Finance 17 (2008) 380–387

Since there appears to be at least some degree of uncertainty over the stationarity properties of the series we elect touse the autoregressive distributed lag (ARDL) bounds test approach to cointegration (Pesaran et al., 2001) which doesnot require the assumption that both series are I(1). In summary, the application of the ARDL bounds test approachentails the estimation of the following regression within the FH framework:

DI

GDP

� �t

¼ a0 þ b1I

GDP

� �t−1

þb2S

GDP

� �t−1

þXni¼1

giDI

GDP

� �t−iþXmj¼0

djDS

GDP

� �t−jþut ð2Þ

where α0 is the drift component and ut are white noise errors. To test for the absence of a long-run relationship betweenthe ratio of gross domestic investment to GDP and the ratio of gross national saving to GDP we employ two separatebounds tests: an F-test for the joint null hypothesis β1=β2=0, and a t-test for the null hypothesis β1=0. Under thealternative hypotheses, there is a stable long-run level relationship between the aforementioned variables which isdescribed by:

IGDP

� �t

¼ #0 þ #1S

GDP

� �t

þut ð3Þ

where, ϑ0=−α0 / β1′ϑ1=−β2 / β1, and ui are mean zero stationary process.Following Pesaran et al. (2001), if the sample test statistics are below the associated lower critical values, the null

hypotheses cannot be rejected, regardless the order of integration of the series. Alternatively, if the test statistics exceedtheir respective upper critical values, the null is rejected in favor of the alternative hypothesis of cointegration. Finally,if the statistics fall within the lower and upper bounds, results are inconclusive.

Once the bounds tests confirm the existence of cointegration, the long-run coefficients in Eq. (3) are estimated by theARDL approach to cointegration of Pesaran and Shin (1999). This involves estimating Eq. (2) by OLS, and then usingselection criteria to determine the number of optimal lagged differences of I

GDP

� �and S

GDP

� �: Hence, Eq. (2) can be

interpreted as an ARDL model which provides inference for the short run dynamics.The bounds test results for the complete sample period are presented in Table 2. For each European country Eq. (2)

is estimated and then the corresponding F- and t-statistics are computed. The lag order is selected according to theSchwarz Bayesian Criterion (SBC). Table 2 also reports the p-value of the Lagrange Multiplier (LM) statistic for serial

Table 2Bounds tests, F- and t-statistics

Countries Lags χ2(1) F-statistic t-statistic Outcome

Austria 0 0.633 8.035a −3.847a CointegrationBelgium 0 0.105 4.923a −2.080c Mixed evidenceDenmark 0 0.846 0.676c −0.820c No cointegrationFinland 1 0.967 2.713c −0.956c No cointegrationFrance 1 0.634 1.973c −1.795c No cointegrationGermany 1 0.758 6.652a −3.588a CointegrationGreece 0 0.152 5.166a −3.214a CointegrationIreland 1 0.107 1.635c −1.807c No cointegrationItaly 3 0.552 9.528a −4.138a CointegrationLuxembourg 2 0.658 15.618a −5.558a CointegrationNetherlands 0 0.724 0.785c −1.250c No cointegrationPortugal 1 0.262 4.645b −2.856b InconclusiveSpain 1 0.379 9.163a −4.224a CointegrationSweden 1 0.660 0.678c −1.090c No cointegrationUK 1 0.890 5.565a −3.317a Cointegration

Notes: the lag order is selected on the basis of SBC. χ2(1) is an LM statistic for testing no residual correlation against order 1. The F-statistic is used totest for the joint significance of the coefficients of the lagged levels in the ARDL-ECM. The t-statistic is used to test for the level of significance of β1 inEq. (2). Asymptotic critical values are obtained from Table CI(iii) Case III: unrestricted intercept and no trend for k=1 (Pesaran et al., 2001, p. 300).a Indicates that the statistic lies above the 0.10 upper bound.b Indicates that it falls within the 0.10 bounds.c Indicates that it lies below the 0.10 lower bound.

384 C. Kollias et al. / International Review of Economics and Finance 17 (2008) 380–387

correlation of first order in the residuals. In all instances, the null hypothesis of no serial correlation cannot be rejected.On the basis of cointegration test results, two groups of countries emerge. The first one is constituted of countries wherecointegration is found. Using the asymptotic critical value bounds computed by Pesaran et al. (2001), the F-statistic liesabove the 0.10 upper bound in eight out of the EU15 countries, namely, Austria, Belgium, Germany, Greece, Italy,Luxembourg, Spain and the UK. Hence the null hypothesis of no long-run relationship is rejected. The correspondingt-statistics confirm this finding with the exception of Belgium. The second group of countries includes those where nocointegration is found, namely, Denmark, Finland, France, Ireland, Sweden and the Netherlands. Finally, the F- and t-test results for the case of Portugal fall in-between the upper and lower bound and we interpret this result as beinginconclusive at the 10% significance level. In general, these findings are hard to explain. Since there is no apparentcommon characteristic shared by the countries in each group, e.g., in terms of size and capital market institutionalfeatures, it is difficult to point to any specific reason that would explain the presence or absence of cointegration.Furthermore, the interpretation of these results is rather ambiguous. If one subscribes to the FH interpretation, thefinding of cointegration (no cointegration) between domestic saving and investment implies low (high) capitalmobility. On the contrary, following the current account solvency argument (Coakley et al., 1996), the finding ofcointegration (no cointegration) implies the effective (ineffective) implementation of government policy actionstargeting a sustainable current account.

Given the findings reported in Table 2, we proceed with the empirical analysis only in the case of the countrieswhere a long-run cointegrating relationship is established. Table 3 presents the estimates of the long-run coefficientsfrom the ARDL specification. These results should be interpreted with caution given the number of usable observations(37) and the resulting low number of degrees of freedom.

The results in Table 3 indicate that the ϑ1 coefficient ranges from −0.11 (Portugal) to 1.64 (Italy). If we accept theFH explanation, we can interpret these findings as follows. Five countries (Austria, Germany, Greece, Spain and theUK) have a coefficient between 0.59 and 0.88, showing evidence of moderate to low capital mobility. For Luxembourgand Portugal the estimated coefficient is not statistically significant. This finding may be interpreted as a signal of highcapital mobility, since statistical insignificance implies that the SI coefficient equals zero. Finally, for Belgium andItaly, the coefficients are significant and above one, thus providing evidence in favour of the Sachsida and Caetano(2000) explanation for the SI correlation, i.e., the substitutability between domestic and external savings. If we turn intothe long-run current account targeting argument, the low, i.e., significantly below one, SI coefficient reported for sixcountries (Austria, Germany, Greece, Luxembourg, Portugal and the UK) indicates that these countries are likely toface current account problems in the future.

2.2. Panel estimation

In order to investigate the causal effects of savings on investment and vice versa in the EU15 countries, a balancedpanel of time series data is constructed, i.e., the same time periods are available for all cross-section units. The variablesof interest are calculated using annual data for the EU15 member states (N=15) during the period 1962–2002 (T=41).Thus, the pooled sample consists of NT=615 observations. Krol (1996) suggests that because of the current accountsolvency issue, cross-sectional investment-saving regressions tend to underestimate international capital mobility. The

Table 3Estimated long-run coefficients, 1962–2002

Countries ARDL Intercept ϑ(S/Y) χ2(1)(p-value)

Austria ARDL (1, 0) 6.66⁎ 0.68⁎⁎ 0.00Belgium ARDL (2, 1) −6.47 1.14⁎⁎ 0.75Germany ARDL (2, 0) 7.30⁎⁎⁎ 0.59⁎⁎ 0.01Greece ARDL (3, 4) 12.71⁎⁎ 0.59⁎⁎ 0.00Italy ARDL (2, 4) −17.33⁎⁎ 1.64⁎⁎ 0.00Luxembourg ARDL (3, 1) 21.09⁎⁎ 0.05 0.00Portugal ARDL (2, 3) 28.55⁎⁎ −0.11 0.00Spain ARDL (3, 4) 3.14 0.88⁎⁎ 0.23UK ARDL (2, 0) 7.49⁎ 0.60⁎⁎ 0.04

Notes: the ARDL lag selection is based on AIC. χ2(1) is a Wald statistic testing the hypothesis that (S/Y) is equal to 1. ⁎ denotes statisticallysignificant at the 5% level; ⁎⁎ denotes statistically significant at the 1% level; ⁎⁎⁎ denotes statistically significant at the 10% level.

Table 4Unit root tests and panel estimates of the SI correlation

IPS panel unit root tests at levels

Statistic p-value

Investment 2.024 0.021Saving 1.466 0.071

Hausman test

c(i) d(t)

0.206 2.934(0.650) (0.086)

Panel estimates (1962–2002)

Adj. Wald-test F-test

β R2 β=1 c(i)=0 d(t)=0

EU15 0.148 0.614 359.879 27.320 11.035(0.001) (0.000) (0.000) (0.000)

EU14 (excl. Lux) 0.157 0.635 230.966 29.348 10.556(0.004) (0.000) (0.000) (0.000)

Notes: marginal significance values in parentheses.

385C. Kollias et al. / International Review of Economics and Finance 17 (2008) 380–387

use of a panel data set of annual observations overcomes this problem. Furthermore, panel data allows to control forboth cross-section and time-period effects. The following panel regression is estimated:

IGDP

� �i;tð Þ¼ aþ c ið Þ þ d tð Þ þ b

IGDP

� �i;tð Þþu i;tð Þ ð4Þ

where the dummy variable c(i) takes on a different value for each country, while d(t) takes on a different value for eachperiod. The first dummy accounts for differences between countries, while the latter accounts for time-related factorscommon to all countries. More specifically, c(i) accounts for country size effects, whereas d(t) allows to control forcommon business cycle effects. Artis and Zhang (1997) provide evidence in favour of increasing synchronisedbusiness cycles in Europe. As a result, saving and investment can move together, independent of the degree of capitalmobility. These time-period effects need to be controlled for empirically.

A Hausman test is performed in order to specify whether these cross- and time-specific parameters are fixed orrandom. The test results indicate that c(i) is a random variable, whereas d(t) is a fixed variable. In order to test thestationarity assumption, we apply the widely used IPS panel unit root test. The estimates of the panel unit root statistics(reported in the upper part of Table 4) indicate that both variables in the panel are stationary at levels at the 10%significance level. The lower part of Table 4 presents the estimation results of Eq. (4) for the whole sample period(1962–2002). The F-statistics test the null hypothesis that either cross-section dummies or time-period effects equalzero. With an F-statistic of 27.320 for c(i) and 11.035 for d(t), we reject the Ho at the 1% level. These findings validatethe specification given in Eq. (4). The point estimate for β is 0.148. To assess the possible bias introduced byLuxembourg in the panel estimation (due to its small size and historical financial openness), we drop this country fromthe sample and re-estimate Eq. (4). Contrary to the findings reported by Jansen (2000), the β-estimate does not changedramatically and it only increases marginally up to 0.157.2 In both instances the β-estimate is far from unity (asevidenced by the Wald test results reported in the lower part of Table 4), hence suggesting that the current accountsolvency is not an important determinant of this correlation. Overall, the panel estimation results indicate a low SIcorrelation in the EU15 countries; a finding which may reflect the combined effects of three factors: (1) higher capitalmobility, (2) lower transaction costs in the international capital markets, and (3) the declining status of long-run currentaccount targeting as a primary government objective. The importance of these factors is expected to have drasticallychanged during the sample period, as capital controls have been largely abolished since the early 1970s, and

2 Similar finding is reported by Ho (2002).

386 C. Kollias et al. / International Review of Economics and Finance 17 (2008) 380–387

macroeconomic policy is less likely to be influenced by current account considerations under a flexible exchange ratesregime.

4. Concluding remarks

A large and growing body of literature has over the years addressed the FH puzzle (Coakley et al. 1998; Frankel,1992). Using the ARDL bounds testing procedure, this study examines the SI correlation in the EU15 membercountries. These countries represent an interesting vehicle for empirical investigation, given their differences in size,economic structure, growth performance and level of development, as well as, in the degree of financial openness.Following the FH interpretation, evidence is found both in favor of high, as well as, of moderate and low capitalmobility, with no particular pattern emerging either in terms of country size, or level of development, or economic andcapital market structure.

In addition, the present study estimates the coefficient of the SI correlation for the EU15 countries using panel datatechniques. The estimation procedure, after controlling for individual (country by country) and temporal (year by year)effects in the data, yields a β-coefficient in the range of 0.148–0.157. If we accept the FH explanation, we can interpretthis finding as evidence of high capital mobility.

Acknowledgements

Insightful comments and constructive suggestions by four anonymous referees of this journal are gratefullyacknowledged by the authors. The usual disclaimer applies.

References

Alexakis, P., & Apergis, N. (1994). The Feldstein–Horioka puzzle and the exchange rate regimes: Evidence from cointegration tests. Journal ofPolicy Modeling, 16, 459−472.

Apergis, N., & Tsoulfidis, L. (1997). The relationship between saving and finance: Theory and evidence from EU countries. Research in Economics,51, 333−358.

Artis, M. J., & Bayoumi, T. (1992). Global capital market integration and the current account. In M. P. Taylor (Ed.), Money and Financial Markets(pp. 297−307). Oxford: Blackwell.

Artis, M. J., & Zhang, W. (1997). International business cycles and the ERM: Is there a European business cycle? International Journal of Financeand Economics, 2, 1−16.

Bajo-Rubio, O. (1998). The saving-investment correlation revisited: The case of Spain. 1964–1994. Applied Economics Letters, 5, 769−777.Banerjee, A., & Zanghieri, P. (2003). A new look at the Feldstein– Horioka puzzle using an integrated panel. CEPPI Working Paper.Baxter, M., & Crucini, M. J. (1993). Explaining saving-investment correlations. American Economic Review, 83, 416−436.Blanchard, O., & Giavazzi, F. (2002). Current account deficits in the euro area: The end of the Feldstein–Horioka puzzle? Brookings Papers on

Economic Activity, 2, 147−186.Caporale, M. G., Panopoulou, E., & Pittis, N. (2005). The Feldstein–Horioka puzzle revisited: A Monte Carlo study. Journal of International Money

and Finance, 24, 1143−1149.Coakley, J., & Kulasi, F. (1997). Cointegration of long span saving and investment. Economics Letters, 54, 1−6.Coakley, J., Kulasi, F., & Smith, R. (1996). Current account solvency and the Feldstein–Horioka puzzle. Economic Journal, 106, 620−627.Coakley, J., Kulasi, F., & Smith, R. (1998). The Feldstein–Horioka puzzle and capital mobility: A review. International Journal of Finance and

Economics, 3, 169−188.Corbin, A. (2001). Country specific effect in the Feldstein–Horioka paradox: A panel data analysis. Economics Letters, 72, 297−302.De Vita, G., & Abbott, A. (2002). Are saving and investment cointegrated? An ARDL bounds testing approach. Economics Letters, 77, 293−299.Dooley, M., Frankel, J., & Mathieson, D. J. (1987). International capital mobility: What do saving-investment correlations tell us? International

Monetary Fund Staff Papers, 34, 503−530.Feldstein, M. (1983). Domestic saving and international capital movements in the long run and the short run. European Economic Review, 21, 129−151.Feldstein, M., & Bachetta, P. (1991). National saving and international investment. In D. Bernheim & J. Shoven (Eds.), National Saving and

Economic Performance (pp. 201−226). Chicago: University of Chicago Press.Feldstein, M., & Horioka, C. (1980). Domestic saving and international capital flows. Economic Journal, 90, 314−329.Frankel, J. A. (1992). Measuring international capital mobility —A review. American Economic Review, 82, 197−202.Georgopoulos, G., & Hejazi, W. (2005). Feldstein–Horioka meets a time trend. Economics Letters, 86, 353−357.Gordon, R. H., & Bovenberg, A. L. (1996). Why is capital so immobile internationally? Possible explanations and implications for capital income

taxation. The American Economic Review, 86, 1057−1075.Harberger, A. C. (1980). Vignettes on the world capital market. American Economic Review Papers and Proceedings, 70, 331−337.Hericourt, J., & Maurel, M. (2005). The Feldstein–Horioka Puzzle Revisited: An European-Regional Perspective. William Davidson Institute

Working Paper, vol. 763.

387C. Kollias et al. / International Review of Economics and Finance 17 (2008) 380–387

Ho, T. W. (2002). The Feldstein–Horioka puzzle revisited. Journal of International Money and Finance, 21, 555−564.Jansen, W. J. (1996). Estimating saving-investment correlations: Evidence for OECD countries based on an error correction model. Journal of

International Money and Finance, 15, 749−781.Jansen, W. J. (1998). Interpreting saving-investment correlations. Open Economies Review, 9, 205−217.Jansen, W. J. (2000). International capital mobility: Evidence from panel data. Journal of International Money and Finance, 19, 507−511.Kim, S. H. (2001). The saving-investment correlation puzzle is still a puzzle. Journal of International Money and Finance, 20, 1017−1034.Krol, R. (1996). International capital mobility: Evidence from panel data. Journal of International Money and Finance, 15, 467−474.Mamingi, N. (1994). Savings-Investment correlations and capital mobility in developing countries. The World Bank Policy Research Working Paper,

vol. 1211.Mendoza, E. G. (1991). Real business cycles in a small open economy. American Economic Review, 81, 797−818.Murphy, R. G. (1984). Capital mobility and the relationship between saving and investment in OECD countries. Journal of International Money and

Finance, 3, 327−342.Obstfeld, M. (1986). Capital mobility in the world economy: Theory and measurement. Carnegie-Rochester Conference Series on Public Policy, 2,

55−104.Obstfeld, M. (1995). International capital mobility in the ‘90s. In P. B. Kenen (Ed.), Understanding Interdependence: The Macroeconomics of the

Open Economy (pp. 201−261). Princeton: Princeton University Press.Pelagidis, T., & Mastroyiannis, T. (2003). The saving-investment correlation in Greece, 1960–1997: Implications for capital mobility. Journal of

Policy Modeling, 25, 609−616.Penati, A., & Dooley, M. (1984, March). Current account imbalances and capital formation in industrial countries 1949–1981. International

Monetary Fund Staff Papers, 31, 1−14.Pesaran, H., & Shin, Y. (1999). An autoregressive distributed lag modeling approach to cointegration analysis. In S. Strom (Ed.), Econometrics and

Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium Cambridge: Cambridge University Press.Pesaran, H., Shin, Y., & Smith, R. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16,

289−326.Sachs, J. D. (1981). The current account and macroeconomic adjustment in the 1970's. Brookings Papers on Economic Activity, 1, 201−268.Sachsida, A., & Caetano, A. (2000). The Feldstein–Horioka puzzle revisited. Economics Letters, 68, 85−88.Shibata, A., & Shintani, M. (1998). Capital mobility in the world economy: An alternative test. Journal if International Money and Finance, 17,

741−756.Sinha, D. (2002). Saving-investment relationships for Japan and other Asian countries. Japan and the World Economy, 14, 1−23.Sinha, D., & Sinha, T. (1998). An exploration of the long-run relationship between saving and investment in the developing economies: A tale of

Latin American countries. Journal of Post Keynesian Economics, 20, 435−443.Sinha, D., & Sinha, T. (2004). The mother of all puzzles would not go away. Economics Letters, 82, 259−267.Tesar, L. (1991). Savings, investment and international capital flows. Journal of International Economics, 31, 29−42.Tesar, L. (1993). International risk- sharing and non-traded goods. Journal of International Economics, 35, 69−89.Vos, R. (1988). Savings, investment and foreign capital flows: Have capital markets become more integrated? Journal of Development Studies, 24,

310−334.