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Forecasting economic and financial variables with global VARs

  • M. Hashem Pesaran
  • Til Schuermann
  • L. Vanessa Smith

This paper considers the problem of forecasting real and financial macroeconomic variables across a large number of countries in the global economy. To this end, a global vector autoregressive (GVAR) model previously estimated over the 1979:Q1-2003:Q4 period by Dees, de Mauro, Pesaran, and Smith (2007) is used to generate out-of-sample one-quarter- and four-quarters-ahead forecasts of real output, inflation, real equity prices, exchange rates, and interest rates over the period 2004:Q1-2005:Q4. Forecasts are obtained for 134 variables from twenty-six regions made up of thirty-three countries and covering about 90 percent of world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. Building on the forecast combination literature, the paper examines the effects of model and estimation uncertainty on forecast outcomes by pooling forecasts obtained from different GVAR models estimated over alternative sample periods. Given the size of the modeling problem and the heterogeneity of the economies considered, industrialized, emerging, and less developed countries, as well as the very real likelihood of multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed, the double-averaged GVAR forecasts performed better than the benchmark forecasts, especially for output, inflation, and real equity prices.

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Paper provided by Federal Reserve Bank of New York in its series Staff Reports with number 317.

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Date of creation: 2008
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Handle: RePEc:fip:fednsr:317
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  1. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, Elsevier.
  2. René Garcia & Pierre Perron, 1995. "An Analysis of the Real Interest Rate Under Regime Shifts," CIRANO Working Papers 95s-05, CIRANO.
  3. Stephane Dees & Filippo di Mauro & M. Hashem Pesaran & L. Vanessa Smith, 2006. "Exploring the International Linkages of the Euro Area: a Global VAR Analysis," Computing in Economics and Finance 2006 47, Society for Computational Economics.
  4. James D. Hamilton & Dong Heon Kim, 2000. "A Re-examination of the Predictability of Economic Activity Using the Yield Spread," NBER Working Papers 7954, National Bureau of Economic Research, Inc.
  5. Pesaran, Mohammad Hashem & Holly, Sean & Dees, Stephane & Smith, L. Vanessa, 2007. "Long Run Macroeconomic Relations in the Global Economy," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 1, pages 1-20.
  6. Alexander Chudik & M. Hashem Pesaran, 2007. "Infinite Dimensional VARs and Factor Models," CESifo Working Paper Series 2176, CESifo Group Munich.
  7. Arturo Estrella & Frederic S. Mishkin, 1995. "Predicting U.S. Recessions: Financial Variables as Leading Indicators," NBER Working Papers 5379, National Bureau of Economic Research, Inc.
  8. Carlo Favero & Iryna Kaminska & Ulf Soderstrom, 2005. "The Predictive Power of the Yield Spread: Further Evidence and a Structural Interpretation," Working Papers 280, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  9. Mackinnon, J.G. & Haug, A.A. & Michelis, L., 1996. "Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration," G.R.E.Q.A.M. 96a09, Universite Aix-Marseille III.
  10. PESARAN M. Hashem & SCHUERMANN Til & WEINER Scott, . "Modelling Regional Interdependencies using a Global Error-Correcting Macroeconometric Model," EcoMod2003 330700121, EcoMod.
  11. Katrin Assenmacher-Wesche & M. Hashem Pesaran, 2008. "Forecasting the Swiss Economy Using Vecx* Models: an Exercise in Forecast Combination Across Models and Observation Windows," National Institute Economic Review, National Institute of Economic and Social Research, vol. 203(1), pages 91-108, January.
  12. Pesaran, M.H. & Pick, A., 2008. "Forecasting Random Walks Under Drift Instability," Cambridge Working Papers in Economics 0814, Faculty of Economics, University of Cambridge.
  13. Francis X. Diebold & Glenn D. Rudebusch & S. Boragan Aruoba, 2004. "The Macroeconomy and the Yield Curve: A Dynamic Latent Factor Approach," NBER Working Papers 10616, National Bureau of Economic Research, Inc.
  14. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809, November.
  15. James H. Stock & Mark W. Watson, 1994. "Evidence on structural instability in macroeconomic times series relations," Working Paper Series, Macroeconomic Issues 94-13, Federal Reserve Bank of Chicago.
  16. Glenn D. Rudebusch & Brian P. Sack & Eric T. Swanson, 2007. "Macroeconomic implications of changes in the term premium," Review, Federal Reserve Bank of St. Louis, issue Jul, pages 241-270.
  17. Alexander Chudik & M. Hashem Pesaran & Elisa Tosetti, 2009. "Weak and Strong Cross Section Dependence and Estimation of Large Panels," CESifo Working Paper Series 2689, CESifo Group Munich.
  18. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  19. Pesaran, M. H. & Shin, Y. & Smith, R. J., 1997. "Structural Analysis of Vector Error Correction Models with Exogenous I(1) Variables," Cambridge Working Papers in Economics 9706, Faculty of Economics, University of Cambridge.
  20. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, 02.
  21. 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.
  22. Alogoskoufis, George & Smith, Ron, 1991. " On Error Correction Models: Specification, Interpretation, Estimation," Journal of Economic Surveys, Wiley Blackwell, vol. 5(1), pages 97-128.
  23. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
  24. Pesaran, M. Hashem & Timmermann, Allan, 2007. "Selection of estimation window in the presence of breaks," Journal of Econometrics, Elsevier, vol. 137(1), pages 134-161, March.
  25. Schorfheide, Frank, 2000. "Forecasting Economic Time Series," Econometric Theory, Cambridge University Press, vol. 16(03), pages 441-450, June.
  26. Clements, Michael P. & Hendry, David F., 2006. "Forecasting with Breaks," Handbook of Economic Forecasting, Elsevier.
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