Academy of Economic Studies, Bucharest Doctoral School of Finance and Banking

1. Academy of Economic Studies, BucharestDoctoral School of Finance and Banking AN ANALYSIS OF THE CONDITIONING ROLE OF FINANCIAL DEVELOPMENT ON THE IMPACT OF EXCHANGE RATE VOLATILITY ON ECONOMIC GROWTH Supervisor: PhD. Univ. Professor Moisă Altăr M. Sc. Student: Petre Dicu Bucharest July 2009

2. This paper examines the influence of real effective exchange rate volatility on GDP per capita growth rate and the way this impact depends on the level of financial development of the countries; • It follows the specification of Aghion, P., P. Bacchetta, R. Ranciere and K. Rogoff (2006) (with regard to regression, variables and estimation techniques); • It conducts a dynamic balanced panel data analysis using GMM dynamic panel data estimators; • It employs a data set for the non-EMU EU countries for a time period of 10 years (1999 - 2008).

3. Structure of the presentation • The estimated model specification • Estimation techniques • Results of analysis and comparisons • Conclusions • Tests

4. The model tested using real data closely follows Aghion, P., P. Bachetta, R. Ranciere and K. Rogoff (2006)yi,t - yi,t-1= (α – 1)yi,t-1 + β1eri,t + β2pci,t*eri,t + β3pci,t + γ1gei,t + γ2iri,t + γ3oti,t + γ4popi,t + µt + ηi + vi,t (1) • yi,t = real GDP per capita, • eri,t = real effective exchange rate volatility, • pci,t = financial development (private credit to GDP), • gei,t = government expenditure, • iri,t = inflation, • oti,t = openness to trade, • popi,t = population growth rate, • ηi = an unobserved individual-specific time-invariant effect for each country, • vi,t = the disturbance term, • µt = a time specific effect, • The series are in logarithms.

5. The overall coefficient of eri,t (the volatility of real exchange rate) is a linear function of pci,t (the financial development): Equation (1) can be written as: yi,t - yi,t-1= (α – 1)yi,t-1 + [β1 + β2pci,t]eri,t + β3pci,t + γ1gei,t + γ2iri,t + γ3oti,t + γ4popi,t + µt + ηi + vi,t (2) • The assumption β1 < 0 and β2 > 0 shows a level of threshold for financial development: pc* = - (β1 / β2), meaning that ∂ (yi,t - yi,t-1)/ ∂ (eri,t) = β1 + β2pci,t > 0 for pci,t > pc* = - (β1 / β2) and ∂ (yi,t - yi,t-1)/ ∂ (eri,t) = β1 + β2pci,t < 0 for pci,t < pc* = - (β1 / β2)

6. GMM estimators are used to address the issue of endogeneity in explanatory variables • I used 2 estimators: • The GMM dynamic panel data estimators developed in Arellano, M. and S. Bond (1991) and Arellano, M. and O. Bover (1995), • Both estimators are based on the assumption of no serial correlation (or limited serial correlation) in the errors, • Both are included in Eviews 6, • They deal with the time specific effect by applying period dummy variables, • They deal with the country specific effect in different ways, as presented below.

7. The GMM estimator from Arellano, M. and S. Bond (1991) (“The first difference GMM dynamic panel estimator”) • The one-step estimator developed in Arellano, M. and S. Bond (1991): • This one step estimator is robust to heteroskedasticity over individuals and over time (Arellano M. and S. Bond (1991), p 285); • The regressions are estimated in first differences; • Differencing the equations is the way the country specific effect is eliminated, since for each country i, Δηi = 0.

8. The GMM estimator from Arellano, M. and O. Bover (1995) (“The orthogonal deviations GMM dynamic panel estimator”) • The orthogonal deviations estimator developed in Arellano, M. and O. Bover (1995): • The transformation that eliminates the individual effect is applied to the first T-1 equations; • It consists of subtracting the mean of the remaining future observations available for each period and using time weights (presented in Arellano, M. and O. Bover (1995), pp 41 and 42); • The regressions remain in levels.

9. Estimations and comparisons versus similar estimations in Aghion, P., P. Bachetta, R. Ranciere and K. Rogoff (2006): * means significant at 1%, ** means significant at 5% and *** means significant at 10% [1] Aghion, P., P. Bacchetta, R. Ranciere and K. Rogoff (2006), p35 [2] Aghion, P., P. Bacchetta, R. Ranciere and K. Rogoff (2006), p35

10. The results are better with the orthogonal deviations GMM estimator in terms of smaller s.e., coefficient and Sargan p-values

11. Estimation results summary: • The orthogonal deviations GMM estimators yield more significant results; (This is in line with the observation in Blundell, R. and S. Bond (1998) (p 115) that the widely used first difference GMM dynamic panel data estimator can be less precise when the autoregressive parameter in the regression is moderately large: The estimated regression: yi,t - yi,t-1= (α – 1)yi,t-1 + …  yi,t = αyi,t-1 + … In the estimations, α – 1 belongs to {-0.2611; -0.1555; -0.0886} => large values for α) • Results presented in the last two columns in the comparative table above indicate that exchange rate volatility has a significant negative impact on economic growth, but only the estimations in the last column indicate a positive and significant interaction term of real exchange rate volatility and financial development; those estimations in the last column are also most reliable among my results, as they result from using the best specified of all estimators.

12. Threshold level analysis: • For the “orthogonal deviations” estimation with significant coefficient for the interaction term, the computed threshold level = 4.68/0.9 = 5.2 => a ratio of e5.2 ≈ 1.81 private credit to GDP, which is a high rate, suggesting it could be biased upwards (supportive of this are the threshold levels of private credit to GDP estimated in Aghion, P., P. Bacchetta, R. Ranciere and K. Rogoff (2006), p 35 - which are 1.01 and 1.10 - significantly smaller than 1.81

13. Conclusions • In the analysis of this paper, instead of looking at an individual effect of exchange rate volatility on growth, an interaction term between volatility of the real effective exchange rate and the financial development level was also used, as in Aghion, P., P. Bacchetta, R. Ranciere and K. Rogoff (2006) • The balanced panel data analysis partly validated the predictions set in the objectives of this paper. • The orthogonal deviations GMM estimators provide evidence that the real effective exchange rate volatility influences the economic growth rate; • The first difference GMM estimator’s results are inconclusive; • The best specified GMM estimator employed yielded results similar to the ones in Aghion, P., P. Bacchetta, R. Ranciere and K. Rogoff (2006): • It provided evidence that the real effective exchange rate volatility influences the economic growth rate and there is a threshold effect of the level of financial development, below which the volatility of the real exchange rate diminishes growth and above which the exchange rate volatility becomes growth enhancing and that the level of financial development should be taken into account when a country chooses to adopt a more flexible or a more fixed exchange rate system.

14. Tests employed - exemplified on the best estimator • Sargan test – for instruments validity • Under the null hypothesis that the over-identifying restrictions are valid, the Sargan statistic (J-statistic) is distributed χ2(d.f. = instrument rank – number of estimated coefficients, including time dummies); • For all tests, the over-identifying restrictions seem to be valid, considering that J-statistic < critical χ2 value => the null will not be rejected; • The p-values show that the validity of the instruments (the null hypothesis) can not be rejected and, in the last estimation, the high value (0.86) suggests almost certain acceptance of the null hypothesis (that the variables are valid).

15. Tests employed - exemplified on the first difference estimator • Wald test of joint significance of the independent variables • Under the null of no relationship, the Wald statistic is asymptotically distributed as χ2(k), where k = the number of estimated coefficients (excluding the time dummies); • The test is robust to general heteroskedasticity (Arellano, M. and S. Bond (1991), p 292; Blundell, Richard and Stephen Bond (1998), p139; Windmeijer, Frank (2005), pp 42 and 43); • For all tests, the Wald statistic >> critical χ2 value => the null will be rejected => a joint significance of the variables used in the regression. P-values of 0.000 confirm that; • Individual coefficient tests can be made, with similar conclusions. The Wald statistic = 86.11 χ2(8, 5%) = 15.507 p-value = 0.000 • The Sargan and Wald tests are also tests presented in Aghion, P., P. Bacchetta, R. Ranciere and K. Rogoff (2006), whose benchmark specification I follow.

16. Testing for error serial correlation - exemplified on the first difference estimator • Since both GMM estimators I use are based on the assumption of limited or no serial correlation of errors, I applied a simple test using the residuals from the GMM estimated equations. I regressed the residuals against their first lag using a panel LS estimator. The results are that errors are not autocorrelated. • For α% significance, the criteria are: DW > DW(U, α%) => the error terms are not positively autocorrelated, and (4 – DW) > DW(U, α%) => the error terms are not negatively autocorrelated. (the critical values of the statistics are taken from Greene, William H. (2003), p 958, Table G.6) • For all three estimations the result is that the errors are not positively nor negatively autocorrelated • We can also assume the noncorrelation of error terms by looking at the values of adjusted R square, which are very small (close to zero). DW = 1.843594 > 1.63 4 – DW = 2.156406 > 1.63 DW (U, 5%, nr of regressors excluding intercept = 1, observations = 66) = 1.63 Adjusted R-square = 0.021369 << 1

17. Selected Bibliography • Aghion, Philippe, Philippe Bacchetta, Romain Ranciere, and Kenneth Rogoff, “Exchange Rate Volatility and Productivity Growth: The Role of Financial Development”, Center for Economic Policy Research, Discussion Paper no. 5629, April 2006 • Arellano, Manuel and Stephen Bond, “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations”, The Review of Economic Studies, vol. 58, no. 2, 277-297, April 1991 • Arellano, Manuel and Olympia Bover, “Another Look at the Instrumental Variable Estimation of Error-components Models”, Journal of Econometrics 68, 29-51, 1995 • Blundell, Richard and Stephen Bond, “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models”, Journal of Econometrics 87, 115-143, 1998 • Bond, Stephen R., “Dynamic Panel Data Models: A Guide to Micro Data Methods and Practice”, CeMMAP Working Paper 09, April 2002 • Dubas, Justin M., Byung-Joo Lee, and Nelson C. Mark, “Effective Exchange Rate Classifications and Growth”, NBER Working Paper 11272, April 2005 • Husain, Aasim M., Ashoka Mody, and Kenneth Rogoff, “Exchange Rate Regime Durability and Performance in Developing versus Advanced Economies”, Journal of Monetary Economics 52, 35-64, 2005 • Levine, Ross, Norman Loayza, and Thorsten Beck, “Financial Intermediation and Growth: Causality and Causes”, Journal of Monetary Economics 46, 31-77, 2000 • Levy-Yeyati, Eduardo and Federico Sturzenegger, “To Float or to Fix: Evidence on the Impact of Exchange Rate Regimes on Growth”, The American Economic Review, vol. 93, no. 4, pp. 1173-1193, Nov. 2003 • Rogoff, Kenneth, Aasim M. Husain, Ashoka Mody, Robin Brooks and Nienke Oomes, “Evolution and Performance of Exchange Rate Regimes”, IMF Working Paper 243, Dec. 2003

18. Back up slides

19. The choice of instrumental variables 1/2 • For the regressors I mention the following possibilities: endogenous (correlated with current and past shocks, but uncorrelated with future shocks); predetermined (correlated with past shocks and uncorrelated with present and future shocks) or strictly exogenous (uncorrelated with past, present or future shocks); • In the assumption of lack of serial correlation, than values of the dependent variable lagged 2 and longer are valid instruments for the regressions in first differences (Arellano, M. and S. Bond (1991), p 278); so are lagged values of 2 or higher order of endogenous regressors in first differenced equations; the predetermined ones from the lag -1 and higher order, and the strictly exogenous can be used for all lags (Bond, Stephen R. (2002), p16; Windmeijer, Frank (2005); Arellano, M. and S. Bond (1991), p 280).

20. The choice of instrumental variables 2/2 • For the orthogonal deviations estimators, the transformation ensures that lags of predetermined variables are valid instruments in the transformed equations and that if the original vi,t are not autocorrelated, than so are the transformed errors; • As with the first difference estimator, limited or lack of serial dependence in vi,t allow for lags of the dependent variable to be predetermined variables in the model and thus to be used as valid instruments.

21. Tests employed - exemplified on the best estimator for AR(4) • The Breusch-Godfrey test for errors serial correlation, using the F statistic: • P-value (F-statistic) = 0.187853 > 0.1 => the null hypothesis (that errors are uncorrelated) can not be rejected ; • F-stat = 1.77 <= 2.943 = critical F (v11=, v2=10) => H0 accepted (errors uncorrelated); • Also by looking at the adjusted R-square, the very small value indicates a misspecified regression, implying the noncorrelation in error terms; • Similar tests can be conducted for AR(r) for all the estimations conducted in this paper’s application.