Achieving shrinkage in a time-varying parameter model framework

Bitto, Angela and Frühwirth-Schnatter, Sylvia (2019) Achieving shrinkage in a time-varying parameter model framework. Journal of Econometrics, 210 (1). pp. 75-97. ISSN 03044076

Available under License Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).

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Shrinkage for time-varying parameter (TVP) models is investigated within a Bayesian framework, with the aim to automatically reduce time-varying Parameters to staticones, if the model is overfitting. This is achieved through placing the double gamma shrinkage prior on the process variances. An efficient Markov chain Monte Carlo scheme is devel- oped, exploiting boosting based on the ancillarity-sufficiency interweaving strategy. The method is applicable both to TVP models for univariate a swell as multivariate time series. Applications include a TVP generalized Phillips curve for EU area inflation modeling and a multivariate TVP Cholesky stochastic volatility model for joint modeling of the Returns from the DAX-30index.

Item Type: Article
Keywords: Bayesian inference, Bayesian Lasso, Double Gamma Prior, Hierarchical Priors, Kalman filter, Log predictive density scores, Normal-gammaprior, Sparsity, State space model
Divisions: Departments > Finance, Accounting and Statistics
Version of the Document: Published
Depositing User: ePub Administrator
Date Deposited: 01 Apr 2019 09:18
Last Modified: 01 Apr 2019 10:04
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