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

Taşpınar S., Doğan O., Chae J., Bera A. K.

Oxford Bulletin of Economics and Statistics, vol.83, no.5, pp.1243-1272, 2021 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 83 Issue: 5
  • Publication Date: 2021
  • Doi Number: 10.1111/obes.12425
  • Journal Name: Oxford Bulletin of Economics and Statistics
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, International Bibliography of Social Sciences, Periodicals Index Online, ABI/INFORM, Business Source Elite, Business Source Premier, CAB Abstracts, EBSCO Education Source, EconLit, Public Affairs Index, vLex, DIALNET
  • Page Numbers: pp.1243-1272
  • Istanbul Technical University Affiliated: Yes


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.