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Bayesian Model Averaging and exchange rate forecasts

  • Jonathan H. Wright

Exchange rate forecasting is hard and the seminal result of Meese and Rogoff (1983) that the exchange rate is well approximated by a driftless random walk, at least for prediction purposes, has never really been overturned despite much effort at constructing other forecasting models. However, in several other macro and financial forecasting applications, researchers in recent years have considered methods for forecasting that combine the information in a large number of time series. One method that has been found to be remarkably useful for out-of-sample prediction is simple averaging of the forecasts of different models. This often seems to work better than the forecasts from any one model. Bayesian Model Averaging is a closely related method that has also been found to be useful for out-of-sample prediction. This starts out with many possible models and prior beliefs about the probability that each model is the true one. It then involves computing the posterior probability that each model is the true one, and averages the forecasts from the different models, weighting them by these posterior probabilities. This is effectively a shrinkage methodology, but with shrinkage over models not just over parameters. I apply this Bayesian Model Averaging approach to pseudo-out-of-sample exchange rate forecasting over the last ten years. I find that it compares quite favorably to a driftless random walk forecast. Depending on the currency-horizon pair, the Bayesian Model Averaging forecasts sometimes do quite a bit better than the random walk benchmark (in terms of mean square prediction error), while they never do much worse. The forecasts generated by this model averaging methodology are however very close to (but not identical to) those from the random walk forecast.

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Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series International Finance Discussion Papers with number 779.

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Date of creation: 2003
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Handle: RePEc:fip:fedgif:779
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  1. Faust, Jon & Rogers, John H. & H. Wright, Jonathan, 2003. "Exchange rate forecasting: the errors we've really made," Journal of International Economics, Elsevier, vol. 60(1), pages 35-59, May.
  2. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
  3. Cheung, Yin-Wong & Chinn, Menzie D. & Pascual, Antonio Garcia, 2005. "Empirical exchange rate models of the nineties: Are any fit to survive?," Journal of International Money and Finance, Elsevier, vol. 24(7), pages 1150-1175, November.
  4. Kilian, Lutz & Taylor, Mark P., 2001. "Why is it so difficult to beat the random walk forecast of exchange rates?," Working Paper Series 0088, European Central Bank.
  5. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
  6. James H. Stock & Mark W. Watson, 2001. "Forecasting Output and Inflation: The Role of Asset Prices," NBER Working Papers 8180, National Bureau of Economic Research, Inc.
  7. Gernot Doppelhofer & Ronald I. Miller & Xavier Sala-i-Martin, 2000. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (Bace) Approach," OECD Economics Department Working Papers 266, OECD Publishing.
  8. Richard A. Meese & Andrew K. Rose, 1991. "An Empirical Assessment of Non-Linearities in Models of Exchange Rate Determination," Review of Economic Studies, Oxford University Press, vol. 58(3), pages 603-619.
  9. Gary Koop & Simon M. Potter, 2003. "Forecasting in large macroeconomic panels using Bayesian Model Averaging," Staff Reports 163, Federal Reserve Bank of New York.
  10. Carmen Fernandez & Eduardo Ley & Mark Steel, 1999. "Model uncertainty in cross-country growth regressions," Econometrics 9903003, EconWPA, revised 06 Oct 2001.
  11. Lutz Kilian, 1998. "Small-Sample Confidence Intervals For Impulse Response Functions," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 218-230, May.
  12. Hjort N.L. & Claeskens G., 2003. "Frequentist Model Average Estimators," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 879-899, January.
  13. Geert Bekaert & Robert J. Hodrick, 1991. "Characterizing Predictable Components in Excess Returns on Equity and Foreign Exchange Markets," NBER Working Papers 3790, National Bureau of Economic Research, Inc.
  14. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
  15. Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-11.
  16. Engel, Charles & Hamilton, James D, 1990. "Long Swings in the Dollar: Are They in the Data and Do Markets Know It?," American Economic Review, American Economic Association, vol. 80(4), pages 689-713, September.
  17. Fernandez-Villaverde, Jesus & Francisco Rubio-Ramirez, Juan, 2004. "Comparing dynamic equilibrium models to data: a Bayesian approach," Journal of Econometrics, Elsevier, vol. 123(1), pages 153-187, November.
  18. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
  19. Dornbusch, Rudiger, 1976. "Expectations and Exchange Rate Dynamics," Journal of Political Economy, University of Chicago Press, vol. 84(6), pages 1161-76, December.
  20. Jan J. J. Groen, 1999. "Long horizon predictability of exchange rates: Is it for real?," Empirical Economics, Springer, vol. 24(3), pages 451-469.
  21. Min, C.K. & Zellner, A., 1992. ""Bayesian and Non-Bayesian Methods for Combining Models and Forecasts with Applications to Forecasting International Growth Rates"," Papers 90-92-23, California Irvine - School of Social Sciences.
  22. Kilian, Lutz, 1999. "Exchange Rates and Monetary Fundamentals: What Do We Learn from Long-Horizon Regressions?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 491-510, Sept.-Oct.
  23. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
  24. Avramov, Doron, 2002. "Stock return predictability and model uncertainty," Journal of Financial Economics, Elsevier, vol. 64(3), pages 423-458, June.
  25. Mark, Nelson C, 1995. "Exchange Rates and Fundamentals: Evidence on Long-Horizon Predictability," American Economic Review, American Economic Association, vol. 85(1), pages 201-18, March.
  26. Bernanke, Ben S. & Boivin, Jean, 2003. "Monetary policy in a data-rich environment," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 525-546, April.
  27. K. J. Martijn Cremers, 2002. "Stock Return Predictability: A Bayesian Model Selection Perspective," Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1223-1249.
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