Economics at your fingertips  

Semiparametric Bayesian inference for time-varying parameter regression models with stochastic volatility

Stefanos Dimitrakopoulos

Economics Letters, 2017, vol. 150, issue C, 10-14

Abstract: We develop a Bayesian semiparametric method to estimate a time-varying parameter regression model with stochastic volatility, where both the error distributions of the observations and parameter-driven dynamics are unspecified. We illustrate our methodology with an application to inflation.

Keywords: Dirichlet process; Markov chain Monte Carlo; Stochastic volatility; Time-varying parameters; Inflation (search for similar items in EconPapers)
JEL-codes: C11 C14 C15 C22 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

Economics Letters is currently edited by Economics Letters Editorial Office

More articles in Economics Letters from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

Page updated 2019-01-25
Handle: RePEc:eee:ecolet:v:150:y:2017:i:c:p:10-14