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Data-based priors for vector autoregressions with drifting coefficients

  • Korobilis, Dimitris

This paper proposes full-Bayes priors for time-varying parameter vector autoregressions (TVP-VARs) which are more robust and objective than existing choices proposed in the literature. We formulate the priors in a way that they allow for straightforward posterior computation, they require minimal input by the user, and they result in shrinkage posterior representations, thus, making them appropriate for models of large dimensions. A comprehensive forecasting exercise involving TVP-VARs of different dimensions establishes the usefulness of the proposed approach.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 53772.

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Date of creation: Jan 2014
Date of revision:
Handle: RePEc:pra:mprapa:53772
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  1. Jan J. J. Groen & Richard Paap & Francesco Ravazzolo, 2009. "Real-Time Inflation Forecasting in a Changing World," Working Paper 2009/16, Norges Bank.
  2. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2013. "Macroeconomic forecasting and structural change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(1), pages 82-101, 01.
  3. Koop, Gary & Korobilis, Dimitris, 2012. "Large time-varying parameter VARs," MPRA Paper 38591, University Library of Munich, Germany.
  4. Miguel Belmonte & Gary Koop & Dimitris Korobilis, 2011. "Hierarchical Shrinkage in Time-Varying Parameter Models," Working Papers 1137, University of Strathclyde Business School, Department of Economics.
  5. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 2(97), pages 436-451, May.
  6. Gary Koop & Dimitris Korobilis, 2009. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Working Paper Series 47_09, The Rimini Centre for Economic Analysis, revised Jan 2009.
  7. Korobilis, Dimitris, 2011. "Hierarchical shrinkage priors for dynamic regressions with many predictors," MPRA Paper 30380, University Library of Munich, Germany.
  8. KOROBILIS, Dimitris, 2011. "VAR forecasting using Bayesian variable selection," CORE Discussion Papers 2011022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  9. Frühwirth-Schnatter, Sylvia & Wagner, Helga, 2010. "Stochastic model specification search for Gaussian and partial non-Gaussian state space models," Journal of Econometrics, Elsevier, vol. 154(1), pages 85-100, January.
  10. Dimitris Korobilis, 2012. "Bayesian forecasting with highly correlated predictors," Working Papers 2012_12, Business School - Economics, University of Glasgow.
  11. Eric Eisenstat & Joshua C.C. Chan & Rodney Strachan, 2014. "Stochastic Model Specification Search for Time-Varying Parameter VARs," Working Paper Series 44_14, The Rimini Centre for Economic Analysis.
  12. Bouriga, Mathilde & Féron, Olivier, 2013. "Estimation of covariance matrices based on hierarchical inverse-Wishart priors," Economics Papers from University Paris Dauphine 123456789/11431, Paris Dauphine University.
  13. A. Bhattacharya & D. B. Dunson, 2011. "Sparse Bayesian infinite factor models," Biometrika, Biometrika Trust, vol. 98(2), pages 291-306.
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