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Bayesian Vector autoregressions (BVARs)

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This RePEc Biblio topic is edited by Domenico Giannone. It was first published on 2017-09-02 06:22:58 and last updated on 2017-09-02 08:26:58.

Introduction by the editor

Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out‐of‐sample forecasts, particularly for models with many variables. A solution to this problem is to use informative priors, in order to shrink the richly parameterized unrestricted model towards a parsimonious naıve benchmark, and thus reduce estimation uncertainty

Most relevant link for this topic

http://en.wikipedia.org/wiki/Bayesian_vector_autoregression

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Most relevant research

  1. 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.
  2. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
  3. Christopher A. Sims, 1993. "A Nine-Variable Probabilistic Macroeconomic Forecasting Model," NBER Chapters,in: Business Cycles, Indicators and Forecasting, pages 179-212 National Bureau of Economic Research, Inc.
  4. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
  5. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
  6. 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.
  7. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, Elsevier.
  8. Gary M. Koop, 2013. "Forecasting with Medium and Large Bayesian VARS," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 177-203, March.
  9. Davide Pettenuzzo & Gary Koop & Dimitris Korobilis, 2016. "Bayesian Compressed Vector Autoregressions," Working Papers 103, Brandeis University, Department of Economics and International Businesss School.
  10. Bańbura, Marta & Giannone, Domenico & Lenza, Michele, 2015. "Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 739-756.
  11. Krueger, Fabian & Clark, Todd E. & Ravazzolo, Francesco, 2015. "Using Entropic Tilting to Combine BVAR Forecasts with External Nowcasts," Working Paper 1439, Federal Reserve Bank of Cleveland.
  12. 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, May.
  13. Mattias Villani, 2009. "Steady-state priors for vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 630-650.
  14. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E, 2016. "Priors for the Long Run," CEPR Discussion Papers 11261, C.E.P.R. Discussion Papers.
  15. Brave, Scott & Butters, R. Andrew & Justiniano, Alejandro, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
  16. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
  17. Gianni Amisano & Andreas Beyer & Michele Lenza, 2010. "Enhancing monetary analysis," Research Bulletin, European Central Bank, vol. 11, pages 2-6.
  18. Ricco, Giovanni & Ellahie, Atif, 2012. "Government Spending Reloaded: Fundamentalness and Heterogeneity in Fiscal SVARs," MPRA Paper 42105, University Library of Munich, Germany.
  19. Luca Dedola & Giulia Rivolta & Livio Stracca, 2016. "If the Fed Sneezes, Who Catches a Cold?," NBER Chapters,in: NBER International Seminar on Macroeconomics 2016 National Bureau of Economic Research, Inc.
  20. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.