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A reappraisal of the evidence on PPP: a systematic investigation into MA roots in panel unit root tests and their implications

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  • Christoph Fischer
  • Daniel Porath

Abstract

Panel unit root tests of real exchange rates - as opposed to univariate tests - usually reject non-stationarity. These tests, however, could be biased if the real exchange rate contained MA roots. Indeed, two independent arguments claim that the real exchange rate, being a sum of a stationary and a non-stationary component, is possibly an ARIMA (1, 1, 1) process. Monte Carlo simulations show, how systematic changes in the parameters of the components, of the test equation and of the correlation matrix affect the size of first and second generation panel unit root tests. Two components of the real exchange rate, the real exchange rate of a single good and a weighted sum of relative prices, are constructed from the data for a panel of countries. Computation of the relevant parameters reveals that panel unit root tests of the real exchange rate are severely oversized, usually much more so than simple ADF tests. Thus, the evidence for PPP from panel unit root tests may be merely due to extreme size biases.
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  • Christoph Fischer & Daniel Porath, 2010. "A reappraisal of the evidence on PPP: a systematic investigation into MA roots in panel unit root tests and their implications," Empirical Economics, Springer, vol. 39(3), pages 767-792, December.
    • Handle: RePEc:spr:empeco:v:39:y:2010:i:3:p:767-792
    DOI: 10.1007/s00181-009-0321-7
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    2. Bernd Schnatz, 2007. "Is reversion to PPP in euro exchange rates non-linear?," International Economics and Economic Policy, Springer, vol. 4(3), pages 281-297, November.

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    More about this item

    Keywords

    Panel unit root test; Purchasing power parity; Real exchange rate; Monte Carlo simulation; F31; C33;
    All these keywords.

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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