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Monetary Exchange Rate Model Revisited: Cointegration And Forecasting In Heterogeneous Panel Data




ABSTRACT This study re-examines the exchange rate-monetary fundamentals link with in a panel data framework. Pure time series and pooled time series-based tests fail to find empirical support for monetary exchange rate models (Sarantis (1994) and Groen (2000)). Using recently developed Panel Data Techniques; we would test the exchange rates and monetary fundamentals in a quarterly panel of 19 countries mostly from developed region extending from 1973.1 to 1997.1. Present analysis would be centered on three issues. First, we test whether exchange rates cointegrated with long run determinants predicted by economic theory. For this purpose, we would be employed Pedroni (1997) and Larsson et al (2001) panel cointegration tests for empirical validation of the study. Second, we will also test the short run implications of exchange rate model. These short run implications will be tested; through adapting the panel VEC model the short run identification schemes of Johansen and Juselius (1994). The last issue is to examine the ability for monetary fundamentals to forecast future exchange rate returns. The present endeavor will follow Mark and Sul (2001) approach for forecasting in the case of Panel Data Testing.

Suggested Citation

  • Muhammad S. Butt & Azhar Iqbal & Nuzhat Ahmad, 2004. "Monetary Exchange Rate Model Revisited: Cointegration And Forecasting In Heterogeneous Panel Data," Econometric Society 2004 Far Eastern Meetings 502, Econometric Society.
  • Handle: RePEc:ecm:feam04:502

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    Panel cointegration; Prediction; Exchange rates.;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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