Choosing Prior Hyperparameters

40 Pages Posted: 25 Aug 2016

See all articles by Pooyan Amir-Ahmadi

Pooyan Amir-Ahmadi

University of Illinois at Urbana-Champaign - Department of Economics

Christian Matthes

Federal Reserve Bank of Richmond

Mu-Chun Wang

Goethe University Frankfurt

Date Written: 2016-08-23

Abstract

Bayesian inference is common in models with many parameters, such as large VAR models, models with time-varying parameters, or large DSGE models. A common practice is to focus on prior distributions that themselves depend on relatively few hyperparameters. The choice of these hyperparameters is crucial because their influence is often 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. In terms of applications, we show via Monte Carlo simulations that in time series models with time-varying parameters and stochastic volatility, our approach can drastically improve on using fixed hyperparameters previously proposed in the literature.

Suggested Citation

Amir-Ahmadi, Pooyan and Matthes, Christian and Wang, Mu-Chun, Choosing Prior Hyperparameters (2016-08-23). FRB Richmond Working Paper No. 16-9. Available at SSRN: https://ssrn.com/abstract=2828581

Pooyan Amir-Ahmadi (Contact Author)

University of Illinois at Urbana-Champaign - Department of Economics ( email )

410 David Kinley Hall
1407 W. Gregory
Urbana, IL 61801
United States

Christian Matthes

Federal Reserve Bank of Richmond ( email )

P.O. Box 27622
Richmond, VA 23261
United States

Mu-Chun Wang

Goethe University Frankfurt ( email )

Gr├╝neburgplatz 1
Frankfurt am Main, 60323
Germany

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