Dimitrakopoulos, S (2017) Semiparametric Bayesian inference for time-varying parameter regression models with stochastic volatility. Economics Letters, 150. pp. 10-14. ISSN 0165-1765
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 Elsevier B.V. All rights reserved. This is an author produced version of a paper published in Economics Letters. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Dirichlet process; Markov chain Monte Carlo; Stochastic volatility; Time-varying parameters; Inflation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Economics Division (LUBS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 28 Jan 2019 10:22 |
Last Modified: | 28 Jan 2019 10:22 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.econlet.2016.10.035 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:141640 |