Time-varying sparsity in dynamic regression models

Maria Kalli and Jim Griffin

Journal of Econometrics, 2014, vol. 178, issue 2, 779-793

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.

Keywords: Time-varying regression; Shrinkage priors; Normal-gamma priors; Markov chain Monte Carlo; Equity premium; Inflation (search for similar items in EconPapers)
JEL-codes: C11 C15 C52 C53 C58 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (40)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:178:y:2014:i:2:p:779-793

DOI: 10.1016/j.jeconom.2013.10.012

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