Bayesian Inference in Spatial Stochastic Volatility Models: An Application to House Price Returns in Chicago*

Süleyman Taşpınar*, Osman DoĞan, Jiyoung Chae, Anil K. Bera

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

In this study, we propose a spatial stochastic volatility model in which the latent log-volatility terms follow a spatial autoregressive process. Though there is no spatial correlation in the outcome equation (the mean equation), the spatial autoregressive process defined for the log-volatility terms introduces spatial dependence in the outcome equation. To introduce a Bayesian Markov chain Monte Carlo (MCMC) estimation algorithm, we transform the model so that the outcome equation takes the form of log-squared terms. We approximate the distribution of the log-squared error terms of the outcome equation with a finite mixture of normal distributions so that the transformed model turns into a linear Gaussian state-space model. Our simulation results indicate that the Bayesian estimator has satisfactory finite sample properties. We investigate the practical usefulness of our proposed model and estimation method by using the price returns of residential properties in the broader Chicago Metropolitan area.

Original languageEnglish
Pages (from-to)1243-1272
Number of pages30
JournalOxford Bulletin of Economics and Statistics
Volume83
Issue number5
DOIs
Publication statusPublished - Oct 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 The Department of Economics, University of Oxford and John Wiley & Sons Ltd

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