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Bayesian VARs: Specification Choices and Forecast Accuracy

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  • Carriero, Andrea
  • Clark, Todd
  • Marcellino, Massimiliano

Abstract

In this paper we discuss how the forecasting performance of Bayesian VARs is affected by a number of specification choices. In the baseline case, we use a Normal-Inverted Wishart (N-IW) prior that, when combined with a (pseudo-) iterated approach, makes the analytical computation of h-step ahead forecasts feasible and simple, in particular when using standard and fixed values for the tightness and the lag length. We then assess the role of the optimal choice of the tightness, of the lag length and of both; compare alternative approaches to h-step ahead forecasting (direct, iterated and pseudo-iterated); discuss the treatment of the error variance and of cross-variable shrinkage; and address a set of additional issues, including the size of the VAR, modeling in levels or growth rates, and the extent of forecast bias induced by shrinkage. We obtain a large set of empirical results, but we can summarize them by saying that we find very small losses (and sometimes even gains) from the adoption of specification choices that make BVAR modeling quick and easy. This finding could therefore further enhance the diffusion of the BVAR as an econometric tool for a vast range of applications.

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Bibliographic Info

Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 8273.

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Date of creation: Feb 2011
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Handle: RePEc:cpr:ceprdp:8273

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Keywords: Bayesian VARs; forecasting; marginal likelihood; prior specification;

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References

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  1. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
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  4. Koop, Gary, 2011. "Forecasting with Medium and Large Bayesian VARs," SIRE Discussion Papers 2011-38, Scottish Institute for Research in Economics (SIRE).
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  6. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2011. "Forecasting large datasets with Bayesian reduced rank multivariate models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 735-761, 08.
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  12. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2008. "Forecasting Exchange Rates with a Large Bayesian VAR," Working Papers 634, Queen Mary, University of London, School of Economics and Finance.
  13. Banbura, Marta & Giannone, Domenico & Reichlin, Lucrezia, 2007. "Bayesian VARs with Large Panels," CEPR Discussion Papers 6326, C.E.P.R. Discussion Papers.
  14. Giannone, Domenico & Lenza, Michele & Momferatou, Daphne & Onorante, Luca, 2014. "Short-term inflation projections: A Bayesian vector autoregressive approach," International Journal of Forecasting, Elsevier, vol. 30(3), pages 635-644.
  15. A. Carriero & G. Kapetanios & M. Marcellino, 2010. "Forecasting Government Bond Yields with Large Bayesian VARs," Economics Working Papers ECO2010/17, European University Institute.
  16. M. Hashem Pesaran & Andreas Pick & Allan Timmermann, 2010. "Variable Selection, Estimation and Inference for Multi-period Forecasting Problems," DNB Working Papers 250, Netherlands Central Bank, Research Department.
  17. Marco Del Negro & Frank Schorfheide, 2002. "Priors from general equilibrium models for VARs," Working Paper 2002-14, Federal Reserve Bank of Atlanta.
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  19. Geweke, John & Whiteman, Charles, 2006. "Bayesian Forecasting," Handbook of Economic Forecasting, Elsevier.
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  22. Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
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Citations

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Cited by:
  1. Koop, Gary & Korobilis, Dimitris, 2012. "Large Time-Varying Parameter VARs," SIRE Discussion Papers 2012-14, Scottish Institute for Research in Economics (SIRE).
  2. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2014. "Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters," International Journal of Forecasting, Elsevier, vol. 30(1), pages 129-143.
  3. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2012. "Prior Selection for Vector Autoregressions," NBER Working Papers 18467, National Bureau of Economic Research, Inc.
  4. Gary Koop, 2013. "Using VARs and TVP-VARs with Many Macroeconomic Variables," Working Papers 1303, University of Strathclyde Business School, Department of Economics.
  5. Todd E. Clark & Michael W. McCracken, 2013. "Evaluating the accuracy of forecasts from vector autoregressions," Working Papers 2013-010, Federal Reserve Bank of St. Louis.
  6. Koop, Gary, 2014. "Forecasting with dimension switching VARs," International Journal of Forecasting, Elsevier, vol. 30(2), pages 280-290.
  7. Brent Meyer & Saeed Zaman, 2013. "It’s not just for inflation: The usefulness of the median CPI in BVAR forecasting," Working Paper 1303, Federal Reserve Bank of Cleveland.
  8. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
  9. Kenneth Beauchemin & Saeed Zaman, 2011. "A medium scale forecasting model for monetary policy," Working Paper 1128, Federal Reserve Bank of Cleveland.
  10. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Paper 1227, Federal Reserve Bank of Cleveland.

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