Time-varying sparsity in dynamic regression models

Journal article


Kalli, M. and Griffin, J. 2014. Time-varying sparsity in dynamic regression models. Journal of Econometrics. 178 (2), pp. 779-793. https://doi.org/10.1016/j.jeconom.2013.10.012
AuthorsKalli, M. and Griffin, J.
Abstract

We propose a novel Bayesian method for dynamic regression models where both the values of the regression coefficients and the importance of the variables are allowed to change over time. The parsimony of the model is important for good forecasting performance and we develop a prior which allows the shrinkage of the regression co-efficients to suitably change over time. An efficient MCMC method for computation is described. The new method is then applied to two forecasting problems in econometrics: equity premium prediction and inflation forecasting. The results show that this method outperforms current competing Bayesian methods.

KeywordsTime-varying regression; shrinkage priors; normal-Gamma priors; Markov chain Monte Carlo; equity premium; inflation.
Year2014
JournalJournal of Econometrics
Journal citation178 (2), pp. 779-793
PublisherElsevier
ISSN0304-4076
Digital Object Identifier (DOI)https://doi.org/10.1016/j.jeconom.2013.10.012
Publication dates
PrintFeb 2014
Publication process dates
Deposited01 Nov 2013
Output statusPublished
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