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Hierarchical shrinkage in time-varying parameter models

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  • Miguel, Belmonte
  • Gary, Koop
  • Dimitris, Korobilis

Abstract

In this paper, we forecast EU-area inflation with many predictors using time-varying parameter models. The facts that time-varying parameter models are parameter-rich and the time span of our data is relatively short motivate a desire for shrinkage. In constant coefficient regression models, the Bayesian Lasso is gaining increasing popularity as an effective tool for achieving such shrinkage. In this paper, we develop econometric methods for using the Bayesian Lasso with time-varying parameter models. Our approach allows for the coefficient on each predictor to be: i) time varying, ii) constant over time or iii) shrunk to zero. The econometric methodology decides automatically which category each coefficient belongs in. Our empirical results indicate the benefits of such an approach.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 31827.

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Date of creation: 20 Jun 2011
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Handle: RePEc:pra:mprapa:31827

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Keywords: Forecasting; hierarchical prior; time-varying parameters; Bayesian Lasso;

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  1. D Agostino, Antonello & Gambetti, Luca & Giannone, Domenico, 2009. "Macroeconomic Forecasting and Structural Change," CEPR Discussion Papers, C.E.P.R. Discussion Papers 7542, C.E.P.R. Discussion Papers.
  2. Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
  3. Cogley, Timothy W. & Morozov, Sergei & Sargent, Thomas J., 2003. "Bayesian fan charts for UK inflation: Forecasting and sources of uncertainty in an evolving monetary system," CFS Working Paper Series 2003/44, Center for Financial Studies (CFS).
  4. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
  5. Sangjoon Kim, Neil Shephard & Siddhartha Chib, . "Stochastic volatility: likelihood inference and comparison with ARCH models," Economics Papers W26, revised version of W, Economics Group, Nuffield College, University of Oxford.
  6. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
  7. Christine De Mol & Domenico Giannone & Lucrezia Reichlin, 2008. "Forecasting using a large number of predictors: is Bayesian shrinkage a valid alternative to principal components?," ULB Institutional Repository 2013/6411, ULB -- Universite Libre de Bruxelles.
  8. Gary Koop & Dimitris Korobilis, 2009. "Forecasting Inflation Using Dynamic Model Averaging," Working Paper Series, The Rimini Centre for Economic Analysis 34_09, The Rimini Centre for Economic Analysis, revised Jan 2009.
  9. 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.
  10. M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006. "Forecasting Time Series Subject to Multiple Structural Breaks," Review of Economic Studies, Oxford University Press, vol. 73(4), pages 1057-1084.
  11. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
  12. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, Blackwell Publishing, vol. 39(s1), pages 3-33, 02.
  13. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 821-852.
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  15. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, Cambridge University Press, number 9780521634809.
  16. Koop, Gary & Leon-Gonzalez, Roberto & Strachan, Rodney W., 2009. "On the evolution of the monetary policy transmission mechanism," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 33(4), pages 997-1017, April.
  17. Koop, Gary & Korobilis, Dimitris, 2010. "Forecasting Inflation Using Dynamic Model Averaging," SIRE Discussion Papers, Scottish Institute for Research in Economics (SIRE) 2010-113, Scottish Institute for Research in Economics (SIRE).
  18. Koop, Gary & Korobilis, Dimitris, 2011. "Forecasting Inflation Using Dynamic Model Averaging," SIRE Discussion Papers, Scottish Institute for Research in Economics (SIRE) 2011-40, Scottish Institute for Research in Economics (SIRE).
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Citations

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Cited by:
  1. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E., 2012. "Prior selection for vector autoregressions," Working Paper Series, European Central Bank 1494, European Central Bank.
  2. Joshua C.C. Chan & Angelia L. Grant, 2014. "Fast Computation of the Deviance Information Criterion for Latent Variable Models," CAMA Working Papers 2014-09, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  3. Rangan Gupta & Mampho P. Modise & Josine Uwilingiye, 2011. "Out-of-Sample Equity Premium Predictability in South Africa: Evidence from a Large Number of Predictors," Working Papers 201122, University of Pretoria, Department of Economics.
  4. Dimitris Korobilis, 2014. "Data-based priors for vector autoregressions with drifting coefficients," Working Papers, Business School - Economics, University of Glasgow 2014_04, Business School - Economics, University of Glasgow.
  5. Rangan Gupta & Shawkat Hammoudeh & Beatrice D. Simo-Kengne & Soodabeh Sarafrazi, 2013. "Can the Sharia-Based Islamic Stock Market Returns be Forecasted Using Large Number of Predictors and Models?," Working Papers 201381, University of Pretoria, Department of Economics.
  6. Joscha Beckmann & Rainer Schüssler, 2014. "Forecasting Exchange Rates under Model and Parameter Uncertainty," CQE Working Papers, Center for Quantitative Economics (CQE), University of Muenster 3214, Center for Quantitative Economics (CQE), University of Muenster.
  7. Kalli, Maria & Griffin, Jim E., 2014. "Time-varying sparsity in dynamic regression models," Journal of Econometrics, Elsevier, Elsevier, vol. 178(2), pages 779-793.
  8. 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.

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