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A New Look at Panel Testing of Stationarity and the PPP Hypothesis

Author

Listed:
  • Jushan Bai

    (Boston College)

  • Serena Ng

    (Johns Hopkins University)

Abstract

This paper uses a decomposition of the data into common and idiosyncratic components to develop procedures that test if these components satisfy the null hypothesis of stationarity. The decomposition also allows us to construct pooled tests that satisfy the cross-section independence assumption. In simulations, tests on the components separately generally have better properties than testing the observed series. However, the results are less than satisfactory, especially in comparison with similar procedures developed for unit root tests. The problem can be traced to the properties of the stationarity test, and is not due to the weakness of the common-idiosyncratic decomposition. We apply both panel stationarity and unit root tests to real exchange rates. We found evidence in support of a large stationary common factor. Rejections of PPP are likely due to non-stationarity of country-specific variations.

Suggested Citation

  • Jushan Bai & Serena Ng, 2001. "A New Look at Panel Testing of Stationarity and the PPP Hypothesis," Boston College Working Papers in Economics 518, Boston College Department of Economics.
    • Handle: RePEc:boc:bocoec:518
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    References listed on IDEAS

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    1. Quah, Danny, 1994. "Exploiting cross-section variation for unit root inference in dynamic data," Economics Letters, Elsevier, vol. 44(1-2), pages 9-19.
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    4. Choi, In, 2001. "Unit root tests for panel data," Journal of International Money and Finance, Elsevier, vol. 20(2), pages 249-272, April.
    5. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    6. Caner, M. & Kilian, L., 2001. "Size distortions of tests of the null hypothesis of stationarity: evidence and implications for the PPP debate," Journal of International Money and Finance, Elsevier, vol. 20(5), pages 639-657, October.
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    Citations

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    Cited by:

    1. Candelon, Bertrand & Dumitrescu, Elena-Ivona & Hurlin, Christophe, 2014. "Currency crisis early warning systems: Why they should be dynamic," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1016-1029.
    2. Reichlin, Lucrezia, 2002. "Factor Models in Large Cross-Sections of Time Series," CEPR Discussion Papers 3285, C.E.P.R. Discussion Papers.
    3. Jushan Bai & Josep Lluís Carrion-I-Silvestre, 2009. "Structural Changes, Common Stochastic Trends, and Unit Roots in Panel Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(2), pages 471-501.
    4. Simón Sosvilla-Rivero & Emma García, "undated". "Purchasing Power Parity Revisited," Working Papers 2003-20, FEDEA.
    5. Jerry Coakley & Ana-Maria Fuertes & Ron Smith, 2002. "A Principal Components Approach to Cross-Section Dependence in Panels," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 B5-3, International Conferences on Panel Data.
    6. Jorge Selaive C. & Valentín Délano T., 2006. "Sovereign Spreads: a Factorial Approach," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 9(1), pages 49-67, April.
    7. Jönsson, Kristian, 2004. "Testing for Stationarity in Panel Data Models when Disturbances are Cross-Sectionally Correlated," Working Papers 2004:17, Lund University, Department of Economics, revised 26 Nov 2004.
    8. Choi, Chi-Young, 2004. "Searching for evidence of long-run PPP from a post-Bretton Woods panel: separating the wheat from the chaff," Journal of International Money and Finance, Elsevier, vol. 23(7-8), pages 1159-1186.

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    More about this item

    Keywords

    Common factors; common trends; principal components; unit root; cointegration;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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