Time-varying sparsity in dynamic regression models

Kalli, Maria and Griffin, Jim E. (2014) Time-varying sparsity in dynamic regression models. Journal of Econometrics, 178 (2). pp. 779-793. ISSN 0304-4076. (doi:https://doi.org/10.1016/j.jeconom.2013.10.012) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Official URL
http://dx.doi.org/10.1016/j.jeconom.2013.10.012

Abstract

A novel Bayesian method for inference in dynamic regression models is proposed where both the values of the regression coefficients and the importance of the variables are allowed to change over time. We focus on forecasting and so the parsimony of the model is important for good performance. A prior is developed which allows the shrinkage of the regression coefficients to suitably change over time and an efficient Markov chain Monte Carlo method for posterior inference is described. The new method is 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
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jim Griffin
Date Deposited: 29 May 2014 15:46 UTC
Last Modified: 01 Dec 2017 09:13 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/41223 (The current URI for this page, for reference purposes)
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