  This paper explores the estimation of a dynamic spatiotemporal autoregressive
conditional heteroscedasticity (ARCH) model. The log-volatility term in this
model can depend on (i) the spatial lag of the log-squared outcome variable,
(ii) the time-lag of the log-squared outcome variable, (iii) the spatiotemporal
lag of the log-squared outcome variable, (iv) exogenous variables, and (v) the
unobserved heterogeneity across regions and time, i.e., the regional and time
fixed effects. We examine the small and large sample properties of two
quasi-maximum likelihood estimators and a generalized method of moments
estimator for this model. We first summarize the theoretical properties of
these estimators and then compare their finite sample properties through Monte
Carlo simulations.
