Bayesian inference in spatial GARCH models: an application to US house price returns

作     者：Dogan, Osman Taspinar, Suleyman 

作者机构：Istanbul Tech Univ Dept Econ Istanbul Turkey CUNY Queens Coll Dept Econ New York NY USA 

出 版 物：《SPATIAL ECONOMIC ANALYSIS》 (空间经济分析)

年 卷 期：2023年第18卷第3期

页      面：410-428页

核心收录：

学科分类：02[经济学] 0201[经济学-理论经济学] 

主　　题：spatial generalized autoregressive conditional heteroskedasticity (SGARCH) volatility spatial autoregressive model spatial dependence Bayesian inference Markov chain Monte Carlo (MCMC) house price returns 

摘      要：In this paper we consider a high-order spatial generalized autoregressive conditional heteroskedasticity (GARCH) model to account for the volatility clustering patterns observed over space. The model consists of a log-volatility equation that includes the high-order spatial lags of the log-volatility term and the squared outcome variable. We use a transformation approach to turn the model into a mixture of normals model, and then introduce a Bayesian Markov chain Monte Carlo (MCMC) estimation approach coupled with a data-augmentation technique. Our simulation results show that the Bayesian estimator has good finite sample properties. We apply a first-order version of the spatial GARCH model to US house price returns at the metropolitan statistical area level over the period 2006Q1-2013Q4 and show that there is significant variation in the log-volatility estimates over space in each period.