Choosing Prior Hyperparameters: With Applications to Time-Varying Parameter Models. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Amir-Ahmadi, Pooyan; Matthes, Christian; Wang, Mu-Chun
• Is Part Of: Journal of business & economic statistics. Volume 38:Issue 1(2020)
• Page Start: 124
• Page End: 136
• Publication Date: 2020-01-02
• Subjects: Bayesian inference -- Bayesian VAR -- Time variation.; Economics -- Statistical methods -- Periodicals; Commercial statistics -- Periodicals; Économie politique -- Méthodes statistiques -- Périodiques; Statistique commerciale -- Périodiques; 330.015195
• DOI: 10.1080/07350015.2018.1459302
• Languages: English
• ISSNs: 0735-0015

Abstract

Abstract : Time-varying parameter models with stochastic volatility are widely used to study macroeconomic and financial data. These models are almost exclusively estimated using Bayesian methods. A common practice is to focus on prior distributions that themselves depend on relatively few hyperparameters such as the scaling factor for the prior covariance matrix of the residuals governing time variation in the parameters. The choice of these hyperparameters is crucial because their influence is sizeable for standard sample sizes. In this article, we treat the hyperparameters as part of a hierarchical model and propose a fast, tractable, easy-to-implement, and fully Bayesian approach to estimate those hyperparameters jointly with all other parameters in the model. We show via Monte Carlo simulations that, in this class of models, our approach can drastically improve on using fixed hyperparameters previously proposed in the literature. Supplementary materials for this article are available online.