Bayesian Inference in Spatial Stochastic Volatility Models: An Application to House Price Returns in Chicago*
提出一个空间随机波动率模型，其中对数波动率服从空间自回归过程，并用贝叶斯MCMC方法估计，模拟显示估计量性质良好，应用于芝加哥大都市区住宅价格回报率数据。
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.