Time-varying sparsity in dynamic regression models

Kalli, M. and Griffin, J. (2014) Time-varying sparsity in dynamic regression models. Journal of Econometrics, 178 (2). pp. 779-793. ISSN 0304-4076.


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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.

Item Type: Article
Uncontrolled Keywords: Time-varying regression; shrinkage priors; normal-Gamma priors; Markov chain Monte Carlo; equity premium; inflation.
Subjects: Q Science > QA Mathematics > QA0273 Probabilities. Mathematical statistics
Divisions: pre Nov-2014 > Faculty of Business and Management > Department of Accounting, Finance & Information Management
Depositing User: Maria Kalli
Date Deposited: 01 Nov 2013 13:16
Last Modified: 31 Jan 2016 18:28
URI: https://create.canterbury.ac.uk/id/eprint/12075

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Last edited: 29/06/2016 12:23:00