Your path in the tree: Start > Econometrics > Time Series Models > VAR Models > Bayesian Vector autoregressions (BVARs)

Bayesian Vector autoregressions (BVARs)

Narrower topics in the RePEc Biblio tree

This RePEc Biblio topic is edited by Domenico Giannone (pgi49). It was first published on 2017-09-02 06:22:58 and last updated on 2020-04-01 03:06:37.

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

Most relevant JEL codes

Most relevant NEP reports

NEP reports are email or RSS notifications about new research in selected fields. Subscriptions are free.

Most relevant research

  1. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983, "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers, National Bureau of Economic Research, Inc, number 1202, Sep.
  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, volume 2, issue 4, pages 497-498.
  3. Christopher A. Sims, 1993, "A Nine-Variable Probabilistic Macroeconomic Forecasting Model," NBER Chapters, National Bureau of Economic Research, Inc, "Business Cycles, Indicators, and Forecasting".
  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, volume 39, issue 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., volume 25, issue 1, pages 71-92, DOI: 10.1002/jae.1137.
  6. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015, "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, volume 97, issue 2, pages 436-451, May.
  7. Karlsson, Sune, 2013, "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, Elsevier, chapter 0, in: G. Elliott & C. Granger & A. Timmermann, "Handbook of Economic Forecasting", DOI: 10.1016/B978-0-444-62731-5.00015-4.
  8. Gary M. Koop, 2013, "Forecasting with Medium and Large Bayesian VARS," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 28, issue 2, pages 177-203, March.
  9. Gary Koop & Dimitris Korobilis & Davide Pettenuzzo, 2016, "Bayesian Compressed Vector Autoregressions," Working Papers, Brandeis School of Business and Economics, number 103, Mar.
  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, volume 31, issue 3, pages 739-756, DOI: 10.1016/j.ijforecast.2014.08.013.
  11. Todd E. Clark & Fabian Krueger & Francesco Ravazzolo, 2015, "Using Entropic Tilting to Combine BVAR Forecasts with External Nowcasts," Working Papers (Old Series), Federal Reserve Bank of Cleveland, number 1439, Jan, DOI: 10.26509/frbc-wp-201439.
  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, volume 45, issue 2, pages 643-673, May.
  13. Mattias Villani, 2009, "Steady-state priors for vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 24, issue 4, pages 630-650, DOI: 10.1002/jae.1065.
  14. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio, 2016, "Priors for the Long Run," CEPR Discussion Papers, Centre for Economic Policy Research, number 11261, May.
  15. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016, "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series, Federal Reserve Bank of Chicago, number WP-2016-5, May.
  16. Frank Schorfheide & Dongho Song, 2015, "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, volume 33, issue 3, pages 366-380, July, DOI: 10.1080/07350015.2014.954707.
  17. Gianni Amisano & Andreas Beyer & Michele Lenza, 2010, "Enhancing monetary analysis," Research Bulletin, European Central Bank, volume 11, pages 2-6.
  18. Ricco, Giovanni & Ellahie, Atif, 2012, "Government Spending Reloaded: Fundamentalness and Heterogeneity in Fiscal SVARs," MPRA Paper, University Library of Munich, Germany, number 42105, Apr.
  19. Luca Dedola & Giulia Rivolta & Livio Stracca, 2016, "If the Fed Sneezes, Who Catches a Cold?," NBER Chapters, National Bureau of Economic Research, Inc, "NBER International Seminar on Macroeconomics 2016".
  20. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015, "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 30, issue 1, pages 46-73, January.