Choosing Prior Hyperparameters
Christian Matthes and
Authors registered in the RePEc Author Service: Pooyan Amir Ahmadi ()
No 16-9, Working Paper from Federal Reserve Bank of Richmond
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.
Pages: 40 pages
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14) Track citations by RSS feed
Downloads: (external link)
https://www.richmondfed.org/-/media/richmondfedorg ... 2016/pdf/wp16-09.pdf Full text (application/pdf)
Our link check indicates that this URL is bad, the error code is: 403 Forbidden
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:fip:fedrwp:16-09
Ordering information: This working paper can be ordered from
Access Statistics for this paper
More papers in Working Paper from Federal Reserve Bank of Richmond Contact information at EDIRC.
Bibliographic data for series maintained by Christian Pascasio ().