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Deconstructing shocks and persistence in OECD real exchange rates1)

  • Syed Abul Basher and Josep Lluís Carrion-i-Silvestre EMAIL logo
Published/Copyright: April 9, 2013
The B.E. Journal of Macroeconomics
From the journal The B.E. Journal of Macroeconomics Volume 13 Issue 1

Abstract

We study the Purchasing Power Parity (PPP) hypothesis and the PPP puzzle for a sample of seventeen OECD economies when real exchange rates (RERs) are subject to multiple structural breaks. Applying recent panel econometric methods, we first show that RERs are found to be I(1) non-stationary processes when the analysis neglects structural breaks, while they are characterized as I(0) stationary stochastic processes when structural breaks are accommodated. This indicates that ignoring structural breaks can lead to model misspecification, which can bias (upward) shocks’ persistence measures. After controlling for structural breaks, our half-life point estimates appear below one year for both idiosyncratic and common measures of persistence of deviation from the changing mean.

Keywords: cross-sectional dependence; multiple structural breaks; panel data stationarity tests; shock persistence; JEL Classification: C32; C33; E31
Corresponding author: Josep Lluís Carrion-i-Silvestre, AQR-IREA Research Group, Department of Econometrics, Statistics and Spanish Economy, University of Barcelona, Av, Diagonal, 690, 08034 Barcelona, Tel: +34 934024598, Fax: +34 93 4021821, e-mail:
  1. 1

    See Perron (1989).

  2. 2

    See e.g., Rossi (2005).

  3. 3

    The specification of the null hypothesis of unit root implies that the theory is false, which is contrary to the notion of PPP.

  4. 4

    Note that the sequential approach in Bai and Perron (1998) can be used here since under the null hypothesis we have that the units are I(0). Consequently, the consistency on the specification of the number and position of the structural breaks is warranted. Furthermore, the test remains consistent against the alternative hypothesis of I(1) as shown, for instance, in Lee, Huang and Shin (1997), Kurozumi (2002) and Carrion-i-Silvestre (2003).

  5. 5

    We are thankful to Takashi Yamagata for making the data available to us. We include the observations for 1973 in our analysis, while Pesaran (2007) starts at 1974.

  6. 6

    We thank Natalia Bailey for providing us the GAUSS code that has helped us to implement the proposal in Bailey, Kapetanios and Pesaran (2012).

  7. 7

    Following Maddala and Wu (1999), we have computed the bootstrap empirical distribution of the statistics using 20,000 replications – we offer the percentiles of interest in Table 1. Further details are given in the Appendix.

  8. 8

    The number of common factors (r) has been estimated using the panel BIC information criterion in Bai and Ng (2002) with up to six common factors.

  9. 9

    The AR regression equation in which the statistic is based uses the t-sig criterion in Ng and Perron (1995) to select the order of the autoregressive correction with up to ten lags.

  10. 10

    Our results are qualitatively different from Harris, Leybourne and McCabe (2005) who are unable to find favorable support for the PPP hypothesis when applying panel stationary test with cross-sectional dependence and structural breaks. One possible reason for the discrepancy of research conclusions is the constrained framework imposed in Papell (2002) and maintained in Harris, Leybourne and McCabe (2005). As mentioned, our results are based on an unconstrained set-up that does not restrict the RERs to return to the levels previous to the structural change.

  11. 11

    See Taylor and Taylor (2004) for discussion related to the PPP puzzle.

  12. 12

    As above, the selection of the order of the autoregressive model is done using the t-sig information criterion in Ng and Perron (1995) with up to ten lags.

  13. 13

    Empirical estimates of the half-life of shocks to the RER may also be biased upward due to (i) temporal aggregation in the data and (ii) nonlinear adjustment of RERs. However, the possibility of nonlinear adjustment could arise due to the presence of structural breaks, which has been cogently addressed in our work. Whereas, the time aggregation problem emphasized by Taylor (2001) is difficult to deal with since one would require long-spans of high frequency data, which may not always be available in panels. Another source of aggregation bias, that might induces a positive bias in persistence estimates, may result from aggregating across different sectors of the economy (Imbs et al. 2005). Recently, Pesaran and Chudik (2012) addressed the problem of aggregation in the context of large linear dynamic panels that allow for a general pattern of cross-section dependence across the individual units. They derived an ‘optimal aggregate function’ that is shown to recover the distributional features of individual (micro) parameters from the aggregate model. Further, the empirical illustration in their paper suggests that in addition to the dynamic heterogeneity of individual units (as emphasized by Imbs et al. 2005), the unobserved common factor persistence is also needed to understand the slow response of aggregate variables to macro shock.

  14. 14

    As the presence of structural breaks gives rise to nonlinear adjustment in real exchange rates, our results are also relevant to a strand of literature that uses nonlinear models to analyze mean-reversion of real exchange rates – see e.g., Sarno and Taylor (2002: pp. 68–73) for a list of related papers. These models support the view that real exchange rates are driven by an arbitrage process, such that the speed of reversion to PPP is an increasing function of the scale of the shock and hence of the divergence from equilibrium. Applying different classes of nonlinear models both Taylor, Peel and Sarno (2001) and Peltonen, Popescu and Sager (2011) observe a substantial reduction in half-life persistence, compared to what was found using linear PPP models.

  15. 1)

    We thank an anonymous referee for constructive comments and the participants at the “Factors Structure for Panels and Multivariate Time Series Data” conference held at the Maastricht University in 2008 for helpful comments. Carrion-i-Silvestre acknowledges financial support for this research under the Spanish Ministerio de Ciencia y Tecnologa grant ECO2011-30260-C03-03. The views expressed here are those of the authors and do not necessarily reflect the official view of the Qatar Central Bank. The errors that remain are solely ours.

Appendix

The paper computes the bootstrap distribution of the panel stationarity test statistics in order to control for cross-section dependence. The approach that has been followed is the one described in Maddala and Wu (1999) considering that our null hypothesis is that the times series in the panel data are I(0). This approach has also been used in other existing papers in the panel data literature – see, for instance, Smith et al. (2004) and Westerlund (2006, 2008). To be specific, we have proceeded in the following steps:

  • Estimate (1) using the procedure described in Section 2, which allows us to get a consistent estimate of ɛi,t and

  • Specify an AR(pi) model to control the dynamics of where the parameters are estimated using OLS method – the order of the AR(pi) is approximated using the t-sig criterion in Ng and Perron (1995) with the maximum order given by pmax=5 lags. As a result, we obtain where denotes the estimated autoregressive polinomial. Note that here we assume that we are under the null hypothesis of I(0), and that each time series has its own dynamics.

  • Obtain by randomly resampling the rows of the fitted (T×N) matrix of residuals Note that resampling the rows of the matrix of estimated residuals preserve the cross-correlation structure of the error term that exist among the units of the panel – see Maddala and Wu (1999: pp. 646). Then, define the centered resampled residuals where TBOOT denotes the sample size used in the resampling. Finally, construct the first set of bootstrap samples recursively using

    given the initial values for t=0,…,1–pi, t=1,…,TBOOT – in the paper we define TBOOT = T+30. This step amounts to the usual sieve bootstrap for i=1,…,N, t=31,…,TBOOT – note that the first 30 observations are discarded to attenuate the effect of the initial conditions.

  • Generate the first bootstrap samples for from

    where are the estimated parameters that have been obtained in the first step, and compute the statistics of interest using the panel data given by the (T×N) matrix applying the procedures described in the previous sections.

  • Replicate the steps 3 and 4 a large number of times (20,000 times in our case) to obtain the empirical distribution of the panel data statistics.

References

Alexius, A. 2005. “Productivity Shocks and Real Exchange Rates.” Journal of Monetary Economics 52: 555–566.10.1016/j.jmoneco.2004.07.003Search in Google Scholar

Andrews, D. W. K., and H. Y. Chen. 1994. “Approximately Median-Unbiased Estimation of Autoregressive Models.” Journal of Business and Economic Statistics 12: 187–204.Search in Google Scholar

Bai, J., and P. Perron. 1998. “Estimating and Testing Linear Models with Multiple Structural Changes.” Econometrica 66: 47–78.10.2307/2998540Search in Google Scholar

Bai, J., and S. Ng. 2002. “Determining the Number of Factors in Approximate Factor Models.” Econometrica 70: 191–221.10.1111/1468-0262.00273Search in Google Scholar

Bai, J., and S. Ng. 2004a. “A New Look at Panel Testing of Stationarity and the PPP Hypothesis.” In Identification and Inference in Econometric Models: Essays in Honor of Thomas J. Rothenberg, edited by D. Andrews and J. Stock. Cambridge: University Press, Cambridge.10.1017/CBO9780511614491.019Search in Google Scholar

Bai, J., and S. Ng. 2004b. “A PANIC Attack on Unit Roots and Cointegration.” Econometrica 72: 1127–1177.10.1111/j.1468-0262.2004.00528.xSearch in Google Scholar

Bailey, N., G. Kapetanios, and M. H. Pesaran. 2012. “Exponent of Cross-Sectional Dependence: Estimation and Inference.” Cambridge working papers in economics. Cambridge: University of Cambridge.10.2139/ssrn.2003631Search in Google Scholar

Banerjee, A., M. Marcellino, and C. Osbat. 2004. “Some Cautions on the Use of Panel Methods for Integrated Series of Macroeconomic Data.” Econometrics Journal 7: 322–340.10.1111/j.1368-423X.2004.00133.xSearch in Google Scholar

Banerjee, A., M. Marcellino, and C. Osbat. 2005. “Testing for PPP: Should we use Panel Methods”. Empirical Economics 30: 77–91.10.1007/s00181-004-0222-8Search in Google Scholar

Basher, S. A., and J. L. Carrion-i-Silvestre. 2009. “Price Level Convergence, Purchasing Power Parity and Multiple Structural Breaks in Panel Data Analysis: An Application to US Cities.” Journal of Time Series Econometrics 1: Article 3.Search in Google Scholar

Basu, S. 1998. “Technology and Business Cycles: How Well do Standard Models Explain the Facts”. In Beyond Shocks: What Causes Business Cycles?, edited by Jeffrey C. Fuhrer and Scott Schuh. Federal Reserve Bank of Boston conference series no. 42, 207–255.Search in Google Scholar

Breitung, J., and B. Candelon. 2005. “Purchasing Power Parity During Currency Crises: A Panel Unit Root Test Under Structural Breaks.” Review of World Economics 141: 124–140.10.1007/s10290-005-0018-8Search in Google Scholar

Caner, M., and L. Kilian. 2001. “Size Distortions of Tests of the Null Hypothesis of Stationarity: Evidence and Implications for the PPP Debate.” Journal of International Money and Finance 20: 639–657.10.1016/S0261-5606(01)00011-0Search in Google Scholar

Carrion-i-Silvestre, J. L. 2003. “Breaking Date Misspecification Error for the Level Shift KPSS Test.” Economics Letters 81: 365–371.10.1016/S0165-1765(03)00219-2Search in Google Scholar

Carrion-i-Silvestre, J. L., T. Del Barrio-Castro, and E. López-Bazo. 2005. “Breaking the Panels: An Application to the GDP Per Capita.” Econometrics Journal 8: 159–175.10.1111/j.1368-423X.2005.00158.xSearch in Google Scholar

Cassel, G. 1918. “Abnormal Deviation in International Exchanges.” Economic Journal 28: 413–415.10.2307/2223329Search in Google Scholar

Cheung, Y-W., and K. S. Lai. 2000. “On Cross-Country Differences in the Persistence of Real Exchange Rates.” Journal of International Economics 50: 375–397.10.1016/S0022-1996(98)00079-8Search in Google Scholar

Choi, C-Y., N. Mark, and D. Sul. 2006. “Unbiased Estimation of the Half-Life to PPP Convergence in Panel Data.” Journal of Money, Credit and Banking 38: 921–938.10.1353/mcb.2006.0052Search in Google Scholar

Dornbusch, R. and T. Vogelsang. 1991. “Real Exchange Rates and Purchasing Power Parity.” In Trade Theory and Economic Reform: North, South and East, Essays in Honor of Bela Balassa, edited by J. De Melo, and A. Sapir. Oxford: Basil Blackwell.Search in Google Scholar

Gadea, M. D., A. Montañés, and M. Reyes. 2004. “The European Union Currencies and the US Dollar: From Post-Bretton-Woods to the Euro.” Journal of International Money and Finance 23: 1109–1136.10.1016/S0261-5606(04)00083-XSearch in Google Scholar

Hadri, K. 2000. “Testing for Stationarity in Heterogeneous Panel Data.” Econometrics Journal 3: 148–161.10.1111/1368-423X.00043Search in Google Scholar

Hansen, B. E. 1999. “The Grid Bootstrap and the Autoregressive Model.” Review of Economics and Statistics 81: 594–607.10.1162/003465399558463Search in Google Scholar

Harris, R. 2001. “Is there a Case for Exchange Rate Induced Productivity Change”. Discussion paper no. 0110. Centre for International Economic Studies, Adelaide.Search in Google Scholar

Harris, D., S. Leybourne, and B. McCabe. 2005. “Panel Stationarity Tests for Purchasing Power Parity with Cross-Sectional Dependence.” Journal of Business and Economic Statistics, 23: 395–409.10.1198/073500105000000090Search in Google Scholar

Im, K.S., J. Lee, and M. Tieslau. 2005. “Panel LM Unit-Root Tests with Level Shifts.” Oxford Bulletin of Economics and Statistics 67: 393–419.10.1111/j.1468-0084.2005.00125.xSearch in Google Scholar

Imbs, J., H. Mumtaz, M. O. Ravn, and H. Rey. 2005. “PPP Strikes Back: Aggregation and the Real Exchange Rate.” Quarterly Journal of Economics 120: 1–43.Search in Google Scholar

Kilian, L. 1998. “Small-Sample Confidence Intervals for Impulse Response Functions.” Review of Economics and Statistics 80: 218–230.10.1162/003465398557465Search in Google Scholar

Kim, J-O., and W. Enders. 1991. “Real and Monetary Causes of Real Exchange Rate Movements in the Pacific Rim.” Southern Economic Journal 57: 1061–1070.10.2307/1060334Search in Google Scholar

Kurozumi, E. 2002. “Testing for Stationarity with a Break.” Journal of Econometrics 108: 63–99.10.1016/S0304-4076(01)00106-3Search in Google Scholar

Kwiatkowski, D., P. C. B. Phillips, P. J. Schmidt, and Y. Shin. 1992. “Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root: How Sure are we that Economic Time Series have a Unit Root.” Journal of Econometrics 54: 159–178.10.1016/0304-4076(92)90104-YSearch in Google Scholar

Lee, J., C. J. Huang, and Y. Shin. 1997. “On Stationary Tests in the Presence of Structural Breaks.” Economics Letters 55: 165–172.10.1016/S0165-1765(97)00073-6Search in Google Scholar

Levin, A., C. F. Lin, and J. Chu. 2002. “Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties.” Journal of Econometrics 108: 1–24.10.1016/S0304-4076(01)00098-7Search in Google Scholar

Lopez, C., C. J. Murray, and D. H. Papell. 2005. “State of the Art Unit Root Tests and Purchasing Power Parity.” Journal of Money, Credit, and Banking 37: 361–369.10.1353/mcb.2005.0022Search in Google Scholar

Lyhagen, J. 2000. “Why not use standard panel unit root test for testing PPP.” Working paper series in economics and finance 413. Stockholm: Stockholm School of Economics.Search in Google Scholar

Maddala, G. S., and S. Wu. 1999. “A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test.” Oxford Bulletin of Economics and Statistics 61: 631–652.10.1111/1468-0084.0610s1631Search in Google Scholar

Murray, C. J., and D. H. Papell. 2005. “Do Panels Help Solve the Purchasing Power Parity Puzzle.” Journal of Business and Economic Statistics, 23: 410–415.10.1198/073500105000000072Search in Google Scholar

Ng, S., and P. Perron. 1995. “Unit Root Tests in ARMA Models with Data Dependent Methods for the Selection of the Truncation lag.” Journal of the American Statistical Association 90: 268–281.10.1080/01621459.1995.10476510Search in Google Scholar

Ng, S. 2006. “Testing Cross-Section Correlation in Panel Data Using Spacings.” Journal of Business and Economic Statistics 24: 12–23.10.1198/073500105000000171Search in Google Scholar

O’Connell, P. G. J. 1998. “The Overvaluation of the Purchasing Power Parity.” Journal of International Economics 44: 1–19.10.1016/S0022-1996(97)00017-2Search in Google Scholar

Papell, D. 2002. “The Great Appreciation, The Great Depreciation, and the Purchasing Power Parity Hypothesis.” Journal of International Economics 57: 51–82.10.1016/S0022-1996(01)00145-3Search in Google Scholar

Papell, D., and R. Prodan. 2006. “Additional Evidence of Long-Run Purchasing Power Parity with Restricted Structural Change.” Journal of Money, Credit and Banking 38: 1329–1350.10.1353/mcb.2006.0073Search in Google Scholar

Peltonen, T. A., A. Popescu, and M. Sager. 2011. “Can Non-Linear Real Shocks Explain the Persistence of PPP Exchange Rate Disequilibria.”. International Journal of Finance & Economics 16: 290–306.10.1002/ijfe.426Search in Google Scholar

Perron, P. 1989. “The Great Crash, The Oil Price Shock, and the Unit Root Hypothesis.” Econometrica 57: 1361–1401.10.2307/1913712Search in Google Scholar

Pesaran, M. H. 2004. “General Diagnostic Tests for Cross Section Dependence in Panels.” Cambridge working papers in economics no. 435. Cambridge: University of Cambridge.10.2139/ssrn.572504Search in Google Scholar

Pesaran, M. H. 2007. “A Simple Panel Unit Root Test in the Presence of Cross Section Dependence.” Journal of Applied Econometrics 22: 265–312.10.1002/jae.951Search in Google Scholar

Pesaran, M. H. 2012. “Testing Weak Cross-Sectional Dependence in Large Panels.” Cambridge working papers in economics. Cambridge: University of Cambridge.10.2139/ssrn.2050312Search in Google Scholar

Pesaran, M. H., and A. Chudik. 2012. “Aggregation in Large Dynamic Panels.” Forthcoming in Journal of Econometrics.10.2139/ssrn.1762572Search in Google Scholar

Pesavento, E., and B. Rossi. 2006. “Small Sample Confidence Intervals for Multivariate IRFs at Long Horizons.” Journal of Applied Econometrics 21: 1135–1155.10.1002/jae.894Search in Google Scholar

Rogoff, K. 1996. “The Purchasing Power Parity.” Journal of Economic Literature, 34: 647–668.Search in Google Scholar

Rossi, B. 2005. “Confidence Intervals for Half-Life Deviations from Purchasing Power Parity.” Journal of Business and Economic Statistics 23: 432–442.10.1198/073500105000000027Search in Google Scholar

Saint-Paul, G. 1993. “Productivity Growth and the Structure of the Business Cycle.” European Economic Review 37: 861–883.10.1016/0014-2921(93)90095-RSearch in Google Scholar

Sarno, L., and M. P. Taylor. 2002. The Economics of Exchange Rates. Cambridge: Cambridge University Press.Search in Google Scholar

Smith V. L., S. Leybourne, T.-H. Kim, and P. Newbold. 2004. “More Powerful Panel Unit Root Tests with an Application to the Mean Reversion in Real Exchange Rates.” Journal of Applied Econometrics 19: 147–170.10.1002/jae.723Search in Google Scholar

Sul D., P. C. B. Phillips, and C. Y. Choi. 2005. “Prewhitening Bias in HAC Estimation.” Oxford Bulletin of Economics and Statistics 67: 517–546.10.1111/j.1468-0084.2005.00130.xSearch in Google Scholar

Taylor, A. M. 2001. “Potential Pitfalls for the Purchasing-Power-Parity Puzzle? Sampling and Specification Biases in Mean-Reversion Tests of the Law of One Price.” Econometrica 69: 473–498.10.1111/1468-0262.00199Search in Google Scholar

Taylor, A. M., and M. P. Taylor. 2004. “The Purchasing Power Parity Debate.” Journal of Economic Perspective 18: 135–158.10.1257/0895330042632744Search in Google Scholar

Taylor, M. P., D. A. Peel, and L. Sarno. 2001. “Nonlinear Mean-Reversion in Real Exchange Rates: Toward a Solution to the Purchasing Power Parity Puzzles.” International Economic Review 42: 1015–1042.10.1111/1468-2354.00144Search in Google Scholar

Wagner, M. 2005. “On PPP, Unit Roots and Panels.” Empirical Economics 35: 229–249.10.1007/s00181-007-0156-zSearch in Google Scholar

Westerlund, J. 2006. “Testing for Panel Cointegration with Multiple Structural Breaks.” Oxford Bulletin of Economics and Statistics 68: 101–132.10.1111/j.1468-0084.2006.00154.xSearch in Google Scholar

Westerlund, J. 2008. “Panel Cointegration Tests of the Fisher Effect.” Journal of Applied Econometrics 23: 193–233.10.1002/jae.967Search in Google Scholar

Published Online: 2013-04-09
Published in Print: 2013-01-01

©2013 by Walter de Gruyter Berlin Boston

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  11. A contribution to the empirics of welfare growth
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  29. Business cycle dynamics across the US states
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  32. Productivity and resource misallocation in Latin America1)
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https://doi.org/10.1515/bejm-2012-0060
Keywords for this article
cross-sectional dependence; multiple structural breaks; panel data stationarity tests; shock persistence; JEL Classification: C32; C33; E31

Articles in the same Issue

  1. Masthead
  2. Masthead
  3. Advances
  4. How have global shocks impacted the real effective exchange rates of individual euro area countries since the euro’s creation?
  5. Employment by age, education, and economic growth: effects of fiscal policy composition in general equilibrium
  6. Overeducation and skill-biased technical change
  7. Strategic wage bargaining, labor market volatility, and persistence
  8. Households’ uncertainty about Medicare policy
  9. Contributions
  10. Deconstructing shocks and persistence in OECD real exchange rates1)
  11. A contribution to the empirics of welfare growth
  12. Development accounting with wedges: the experience of six European countries
  13. Implementation cycles, growth and the labor market
  14. International technology adoption, R&D, and productivity growth
  15. Bequest taxes, donations, and house prices
  16. Business cycle accounting of the BRIC economies
  17. Privately optimal severance pay
  18. Small business loan guarantees as insurance against aggregate risks
  19. Output growth and unexpected government expenditures
  20. International business cycles and remittance flows
  21. Effects of productivity shocks on hours worked: UK evidence
  22. A prior predictive analysis of the effects of Loss Aversion/Narrow Framing in a macroeconomic model for asset pricing
  23. Exchange rate pass-through and fiscal multipliers
  24. Credit demand, credit supply, and economic activity
  25. Distortions, structural transformation and the Europe-US income gap
  26. Monetary policy shocks and real commodity prices
  27. Topics
  28. News-driven international business cycles
  29. Business cycle dynamics across the US states
  30. Required reserves as a credit policy tool
  31. The macroeconomic effects of the 35-h workweek regulation in France
  32. Productivity and resource misallocation in Latin America1)
  33. Information and communication technologies over the business cycle
  34. In search of lost time: the neoclassical synthesis
  35. Divorce laws and divorce rate in the US
  36. Is the “Great Recession” really so different from the past?
  37. Monetary business cycle accounting for Sweden
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