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Forecasting economic and financial variables with global VARs

Author

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

Abstract

This paper considers the problem of forecasting economic and financial variables across a large number of countries in the global economy. To this end a global vector autoregressive (GVAR) model, previously estimated by Dees, di Mauro, Pesaran, and Smith (2007) and Dees, Holly, Pesaran, and Smith (2007) over the period 1979Q1-2003Q4, is used to generate out-of-sample forecasts one and four quarters ahead for real output, inflation, real equity prices, exchange rates and interest rates over the period 2004Q1-2005Q4. Forecasts are obtained for 134 variables from 26 regions, which are made up of 33 countries and cover about 90% of the world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. Building on the forecast combination literature, the effects of model and estimation uncertainty on forecast outcomes are examined by pooling forecasts obtained from different GVAR models estimated over alternative sample periods. Given the size of the modelling problem, and the heterogeneity of the economies considered-industrialised, emerging, and less developed countries-as well as the very real likelihood of possibly multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed, the double-averaged GVAR forecasts perform better than the benchmark competitors, especially for output, inflation and real equity prices.

Suggested Citation

  • Pesaran, M. Hashem & Schuermann, Til & Smith, L. Vanessa, 2009. "Forecasting economic and financial variables with global VARs," International Journal of Forecasting, Elsevier, vol. 25(4), pages 642-675, October.
  • Handle: RePEc:eee:intfor:v:25:y:2009:i:4:p:642-675
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    1. MacKinnon, James G & Haug, Alfred A & Michelis, Leo, 1999. "Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 563-577, Sept.-Oct.
    2. M. Hashem Pesaran & Andreas Pick, 2008. "Forecasting Random Walks Under Drift Instability," CESifo Working Paper Series 2293, CESifo Group Munich.
    3. Clements, Michael P. & Hendry, David F., 2006. "Forecasting with Breaks," Handbook of Economic Forecasting, Elsevier.
    4. Filippo di Mauro & L. Vanessa Smith & Stephane Dees & M. Hashem Pesaran, 2007. "Exploring the international linkages of the euro area: a global VAR analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., pages 1-38.
    5. Pesaran, M. Hashem & Shin, Yongcheol & Smith, Richard J., 2000. "Structural analysis of vector error correction models with exogenous I(1) variables," Journal of Econometrics, Elsevier, pages 293-343.
    6. Katrin Assenmacher-Wesche & M. Hashem Pesaran, 2008. "Forecasting the Swiss Economy Using VECX* Models: An Exercise in Forecast Combination Across Modelsand Observation Windows," Working Papers 2008-03, Swiss National Bank.
    7. Arturo Estrella & Frederic S. Mishkin, 1998. "Predicting U.S. Recessions: Financial Variables As Leading Indicators," The Review of Economics and Statistics, MIT Press, pages 45-61.
    8. Hamilton, James D & Kim, Dong Heon, 2002. "A Reexamination of the Predictability of Economic Activity Using the Yield Spread," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(2), pages 340-360, May.
    9. Banbura, Marta & Rünstler, Gerhard, 2011. "A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP," International Journal of Forecasting, Elsevier, pages 333-346.
    10. Pesaran M.H. & Schuermann T. & Weiner S.M., 2004. "Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 129-162, April.
    11. Garcia, Rene & Perron, Pierre, 1996. "An Analysis of the Real Interest Rate under Regime Shifts," The Review of Economics and Statistics, MIT Press, pages 111-125.
    12. 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, February.
    13. 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.
    14. Chudik, Alexander & Pesaran, M. Hashem, 2011. "Infinite-dimensional VARs and factor models," Journal of Econometrics, Elsevier, pages 4-22.
    15. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    16. Favero, Carlo A. & Kaminska, Iryna & Söderström, Ulf, 2005. "The Predictive Power of the Yield Spread: Further Evidence and A Structural Interpretation," CEPR Discussion Papers 4910, C.E.P.R. Discussion Papers.
    17. Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
    18. Alexander Chudik & M. Hashem Pesaran & Elisa Tosetti, 2011. "Weak and strong cross‐section dependence and estimation of large panels," Econometrics Journal, Royal Economic Society, vol. 14(1), pages 45-90, February.
    19. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809, November.
    20. 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.
    21. Schorfheide, Frank, 2000. "Forecasting Economic Time Series," Econometric Theory, Cambridge University Press, vol. 16(03), pages 441-450, June.
    22. Chudik, Alexander & Pesaran, Hashem, 2009. "Infinite-dimensional VARs and factor models," Working Paper Series 998, European Central Bank.
    23. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, Elsevier.
    24. 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.
    25. Pesaran, M. Hashem & Timmermann, Allan, 2007. "Selection of estimation window in the presence of breaks," Journal of Econometrics, Elsevier, pages 134-161.
    26. Chudik, Alexander & Pesaran, M. Hashem, 2011. "Infinite-dimensional VARs and factor models," Journal of Econometrics, Elsevier, pages 4-22.
    27. Alogoskoufis, George & Smith, Ron, 1991. " On Error Correction Models: Specification, Interpretation, Estimation," Journal of Economic Surveys, Wiley Blackwell, vol. 5(1), pages 97-128.
    28. Diebold, Francis X. & Rudebusch, Glenn D. & Borag[caron]an Aruoba, S., 2006. "The macroeconomy and the yield curve: a dynamic latent factor approach," Journal of Econometrics, Elsevier, pages 309-338.
    29. 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.
    30. Edward J. Green & Richard M. Todd, 2001. "Thoughts on the Fed's role in the payments system," Annual Report, Federal Reserve Bank of Minneapolis, issue Apr, pages 6-27.
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    More about this item

    Keywords

    Forecasting using GVAR Structural breaks and forecasting Average forecasts across models and windows Financial and macroeconomic forecasts;

    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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