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Hierarchical Shrinkage in Time‐Varying Parameter Models

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  • Miguel A.G. 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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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 33 (2014)
Issue (Month): 1 (01)
Pages: 80-94

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Handle: RePEc:wly:jforec:v:33:y:2014:i:1:p:80-94

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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  1. 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).
  2. Gary Koop & Dimitris Korobilis, 2009. "Forecasting Inflation Using Dynamic Model Averaging," Working Paper Series 34_09, The Rimini Centre for Economic Analysis, revised Jan 2009.
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  8. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "Forecasting Using a Large Number of Predictors: Is Bayesian Regression a Valid Alternative to Principal Components?," CEPR Discussion Papers 5829, C.E.P.R. Discussion Papers.
  9. 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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Cited by:
  1. Dimitris Korobilis, 2014. "Data-based priors for vector autoregressions with drifting coefficients," Working Papers 2014_04, Business School - Economics, University of Glasgow.
  2. Rangan Gupta & Shawkat Hammoudeh & Beatrice D. Simo-Kengne & Soodabeh Sarafrazi, 2014. "Can the Sharia-based Islamic stock market returns be forecasted using large number of predictors and models?," Applied Financial Economics, Taylor & Francis Journals, vol. 24(17), pages 1147-1157, September.
  3. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E, 2012. "Prior Selection for Vector Autoregressions," CEPR Discussion Papers 8755, C.E.P.R. Discussion Papers.
  4. 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.
  5. 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.
  6. 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.
  7. Kalli, Maria & Griffin, Jim E., 2014. "Time-varying sparsity in dynamic regression models," Journal of Econometrics, Elsevier, vol. 178(2), pages 779-793.

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