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Evaluating a global vector autoregression for forecasting

  • Neil R. Ericsson
  • Erica L. Reisman

Global vector autoregressions (GVARs) have several attractive features: multiple potential channels for the international transmission of macroeconomic and financial shocks, a standardized economically appealing choice of variables for each country or region examined, systematic treatment of long-run properties through cointegration analysis, and flexible dynamic specification through vector error correction modeling. Pesaran, Schuermann, and Smith (2009) generate and evaluate forecasts from a paradigm GVAR with 26 countries, based on Dées, di Mauro, Pesaran, and Smith (2007). The current paper empirically assesses the GVAR in Dées, di Mauro, Pesaran, and Smith (2007) with impulse indicator saturation (IIS)—a new generic procedure for evaluating parameter constancy, which is a central element in model-based forecasting. The empirical results indicate substantial room for an improved, more robust specification of that GVAR. Some tests are suggestive of how to achieve such improvements.

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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 1056.

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Date of creation: 2012
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Handle: RePEc:fip:fedgif:1056
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  1. 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.
  2. Frank Smets & Raf Wouters, 2007. "Shocks and Frictions in US Business Cycles : a Bayesian DSGE Approach," Working Paper Research 109, National Bank of Belgium.
  3. Hiebert, Paul & Vansteenkiste, Isabel, 2007. "International trade, technological shocks and spillovers in the labour market: A GVAR analysis of the US manufacturing sector," Working Paper Series 0731, European Central Bank.
  4. Pesaran, M. Hashem & Schuermann, Til & Smith, L. Vanessa, 2009. "Rejoinder to comments on forecasting economic and financial variables with global VARs," International Journal of Forecasting, Elsevier, vol. 25(4), pages 703-715, October.
  5. Erceg, Christopher & Guerriei, Luca & Gust, Christopher, 2006. "SIGMA: A New Open Economy Model for Policy Analysis," MPRA Paper 813, University Library of Munich, Germany.
  6. Jennifer L. Castle & David F. Hendry, 2010. "Nowcasting from disaggregates in the face of location shifts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 200-214.
  7. Pesaran, M.H. & Weiner, S.M., 2001. "Modelling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model," Cambridge Working Papers in Economics 0119, Faculty of Economics, University of Cambridge.
  8. Kevin D. Hoover & Stephen J. Perez, . "Truth and Robustness in Cross-country Growth Regressions," Department of Economics 01-01, California Davis - Department of Economics.
  9. Juselius, Katarina, 1992. "Domestic and foreign effects on prices in an open economy: The case of Denmark," Journal of Policy Modeling, Elsevier, vol. 14(4), pages 401-428, August.
  10. Garratt, Anthony & Lee, Kevin & Pesaran, M. Hashem & Shin, Yongcheol, 2006. "Global and National Macroeconometric Modelling: A Long-Run Structural Approach," OUP Catalogue, Oxford University Press, number 9780199296859, March.
  11. M. Hashem Pesaran & Ron Smith, 2006. "Macroeconometric Modelling with a Global Perspective," IEPR Working Papers 06.43, Institute of Economic Policy Research (IEPR).
  12. David Hendry & Hans-Martin Krolzig, 2003. "The Properties of Automatic Gets Modelling," Economics Series Working Papers 2003-W14, University of Oxford, Department of Economics.
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  16. Alexander Chudik & M. Hashem Pesaran, 2007. "Infinite Dimensional VARs and Factor Models," CESifo Working Paper Series 2176, CESifo Group Munich.
  17. Neil R. Ericsson & David F. Hendry & Grayham E. Mizon, 1998. "Exogeneity, cointegration, and economic policy analysis," International Finance Discussion Papers 616, Board of Governors of the Federal Reserve System (U.S.).
  18. 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.
  19. Hans-Martin Krolzig, 2000. "Computer Automation of General-to-Specific Model Selection Procedures," Econometric Society World Congress 2000 Contributed Papers 0411, Econometric Society.
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  21. Juselius, Katarina, 2006. "The Cointegrated VAR Model: Methodology and Applications," OUP Catalogue, Oxford University Press, number 9780199285679, March.
  22. Castrén, Olli & Dées, Stéphane & Zaher, Fadi, 2010. "Stress-testing euro area corporate default probabilities using a global macroeconomic model," Journal of Financial Stability, Elsevier, vol. 6(2), pages 64-78, June.
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  26. M. Hashem Pesaran & L. Vanessa Smith & Ron P. Smith, 2007. "What if the UK or Sweden had joined the euro in 1999? An empirical evaluation using a Global VAR," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 12(1), pages 55-87.
  27. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501, March.
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  30. Donald W.K. Andrews, 1990. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Cowles Foundation Discussion Papers 943, Cowles Foundation for Research in Economics, Yale University.
  31. Jennifer L. Castle & Nicholas W.P. Fawcett & David F. Hendry, 2009. "Nowcasting Is Not Just Contemporaneous Forecasting," National Institute Economic Review, National Institute of Economic and Social Research, vol. 210(1), pages 71-89, October.
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