Journal Article

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

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.

Attached files

Authors

Dimitrakopoulos, Stefanos

Oxford Brookes departments

Faculty of Business\Department of Accounting, Finance and Economics

Dates

Year of publication: 2016
Date of RADAR deposit: 2017-10-20

---

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

---

Related resources

This RADAR resource is the Accepted Manuscript of Semiparametric Bayesian inference for time-varying parameter regression models with stochastic volatility