Choosing Prior Hyperparameters: With Applications To Time-Varying Parameter Models

Mu-Chun Wang

VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy from Verein für Socialpolitik / German Economic Association

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 paper 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.

Keywords: Bayesian inference; Bayesian VAR; Time variation (search for similar items in EconPapers)
JEL-codes: C11 (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-ecm and nep-ore
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Citations: View citations in EconPapers (16)

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Journal Article: Choosing Prior Hyperparameters: With Applications to Time-Varying Parameter Models (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:vfsc18:181621

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