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Data-based priors for vector autoregressions with drifting coefficients

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  • Korobilis, Dimitris

Abstract

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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Bibliographic Info

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
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Handle: RePEc:pra:mprapa:53772

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Keywords: TVP-VAR; shrinkage; data-based prior; forecasting;

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  1. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2009. "Macroeconomic Forecasting and Structural Change," Working Papers ECARES, ULB -- Universite Libre de Bruxelles 2009_020, ULB -- Universite Libre de Bruxelles.
  2. 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.
  3. Eric Eisenstat & Joshua C.C. Chan & Rodney W. Strachan, 2014. "Stochastic Model Specification Search for Time-Varying Parameter VARs," CAMA Working Papers 2014-23, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  4. Dimitris Korobilis, 2010. "VAR Forecasting Using Bayesian Variable Selection," Working Paper Series, The Rimini Centre for Economic Analysis 51_10, The Rimini Centre for Economic Analysis, revised Apr 2011.
  5. Koop, Gary & Korobilis, Dimitris, 2012. "Large time-varying parameter VARs," MPRA Paper 38591, University Library of Munich, Germany.
  6. Dimitris Korobilis, 2012. "Bayesian Forecasting with Highly Correlated Predictors," Working Paper Series, The Rimini Centre for Economic Analysis 67_12, The Rimini Centre for Economic Analysis.
  7. Frühwirth-Schnatter, Sylvia & Wagner, Helga, 2010. "Stochastic model specification search for Gaussian and partial non-Gaussian state space models," Journal of Econometrics, Elsevier, Elsevier, vol. 154(1), pages 85-100, January.
  8. Korobilis, Dimitris, 2013. "Hierarchical shrinkage priors for dynamic regressions with many predictors," International Journal of Forecasting, Elsevier, Elsevier, vol. 29(1), pages 43-59.
  9. Domenico Giannone & Michèle Lenza & Giorgio E. Primiceri, 2012. "Prior Selection for Vector Autoregressions," Working Papers ECARES, ULB -- Universite Libre de Bruxelles ECARES 2012-002, ULB -- Universite Libre de Bruxelles.
  10. Miguel A.G. Belmonte & Gary Koop & Dimitris Korobilis, 2014. "Hierarchical Shrinkage in Time‐Varying Parameter Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 80-94, 01.
  11. Koop, Gary & Korobilis, Dimitris, 2012. "Large Time-Varying Parameter VARs," SIRE Discussion Papers, Scottish Institute for Research in Economics (SIRE) 2012-14, Scottish Institute for Research in Economics (SIRE).
  12. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, now publishers, vol. 3(4), pages 267-358, July.
  13. A. Bhattacharya & D. B. Dunson, 2011. "Sparse Bayesian infinite factor models," Biometrika, Biometrika Trust, Biometrika Trust, vol. 98(2), pages 291-306.
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