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Data-based priors for vector autoregressions with drifting coefficients

  • Dimitris Korobilis

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 Business School - Economics, University of Glasgow in its series Working Papers with number 2014_04.

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Date of creation: Jan 2014
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Handle: RePEc:gla:glaewp:2014_04
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  1. 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.
  2. Korobilis, Dimitris, 2013. "Hierarchical shrinkage priors for dynamic regressions with many predictors," International Journal of Forecasting, Elsevier, vol. 29(1), pages 43-59.
  3. Korobilis, Dimitris, 2012. "Bayesian forecasting with highly correlated predictors," SIRE Discussion Papers 2012-80, Scottish Institute for Research in Economics (SIRE).
  4. Jan J. J. Groen & Richard Paap & Francesco Ravazzolo, 2009. "Real-time inflation forecasting in a changing world," Staff Reports 388, Federal Reserve Bank of New York.
  5. Gambetti, Luca & D’Agostino, Antonello & Giannone, Domenico, 2010. "Macroeconomic forecasting and structural change," Working Paper Series 1167, European Central Bank.
  6. 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.
  7. Gary Koop & Dimitris Korobilis, 2012. "Large time-varying parameter VARs," Working Papers 2012_04, Business School - Economics, University of Glasgow.
  8. Miguel, Belmonte & Gary, Koop & Dimitris, Korobilis, 2011. "Hierarchical shrinkage in time-varying parameter models," MPRA Paper 31827, University Library of Munich, Germany.
  9. 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.
  10. Dimitris Korobilis, 2013. "Var Forecasting Using Bayesian Variable Selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 204-230, 03.
  11. 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.
  12. A. Bhattacharya & D. B. Dunson, 2011. "Sparse Bayesian infinite factor models," Biometrika, Biometrika Trust, vol. 98(2), pages 291-306.
  13. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E., 2012. "Prior selection for vector autoregressions," Working Paper Series 1494, European Central Bank.
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