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Bayesian VARs: specification choices and forecast accuracy

Author Info

  • Andrea Carriero
  • Todd E. Clark
  • Massimiliano Marcellino

Abstract

In this paper we examine 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 prior that, when combined with a (pseudo-) iterated approach, makes the analytical computation of multi-step 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 multi-step 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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Paper provided by Federal Reserve Bank of Cleveland in its series Working Paper with number 1112.

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Date of creation: 2011
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Handle: RePEc:fip:fedcwp:1112
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Keywords: Bayesian statistical decision theory ; Forecasting ; Vector autoregression;

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Find related papers by JEL classification:
  • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
  • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
  • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
  • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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  1. Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
  2. Gary Koop, 2011. "Forecasting with Medium and Large Bayesian VARs," Working Papers 1117, University of Strathclyde Business School, Department of Economics.
  3. 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.
  4. Domenico Giannone & Martha Banbura & Lucrezia Reichlin, 2008. "Bayesian VARs with large panels," ULB Institutional Repository 2013/13388, ULB -- Universite Libre de Bruxelles.
  5. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
  6. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2010. "Forecasting Government Bond Yields with Large Bayesian VARs," CEPR Discussion Papers 7796, C.E.P.R. Discussion Papers.
  7. 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.
  8. Todd E. Clark & Michael W. McCracken, 2007. "Forecasting with small macroeconomic VARs in the presence of instabilities," Finance and Economics Discussion Series 2007-41, Board of Governors of the Federal Reserve System (U.S.).
  9. Domenico Giannone & Michèle Lenza & Daphné Momferatu & Luca Onorante, 2010. "Short-term inflation projections: a Bayesian vector autoregressive approach," Working Papers ECARES ECARES 2010-011, ULB -- Universite Libre de Bruxelles.
  10. Geweke, John & Whiteman, Charles, 2006. "Bayesian Forecasting," Handbook of Economic Forecasting, Elsevier.
  11. Bauwens, Luc & Lubrano, Michel & Richard, Jean-Francois, 2000. "Bayesian Inference in Dynamic Econometric Models," OUP Catalogue, Oxford University Press, number 9780198773139, December.
  12. Jacobson, Tor & Karlsson, Sune, 2002. "Finding Good Predictors for Inflation: A Bayesian Model Averaging Approach," Working Paper Series 138, Sveriges Riksbank (Central Bank of Sweden).
  13. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
  14. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," Economics Working Papers ECO2009/31, European University Institute.
  15. 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.
  16. Christopher A. Sims & Tao Zha, 1996. "Bayesian methods for dynamic multivariate models," FRB Atlanta Working Paper 96-13, Federal Reserve Bank of Atlanta.
  17. Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
  18. Jonathan H. Wright, 2009. "Forecasting US inflation by Bayesian model averaging," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(2), pages 131-144.
  19. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
  20. Marco Del Negro & Frank Schorfheide, 2004. "Priors from General Equilibrium Models for VARS," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(2), pages 643-673, 05.
  21. Clements, Michael P & Hendry, David F, 1996. "Intercept Corrections and Structural Change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 475-94, Sept.-Oct.
  22. Lewis, Kurt F. & Whiteman, Charles H., 2006. "Empirical Bayesian density forecasting in Iowa and shrinkage for the Monte Carlo era," Discussion Paper Series 1: Economic Studies 2006,28, Deutsche Bundesbank, Research Centre.
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  1. > Econometrics > Time Series Models > VAR Models > Bayesian Vector autoregressions (BVARs)
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