Dynamic spatial autoregressive models with autoregressive and heteroskedastic disturbances
提出一类新的时空数据模型，通过引入时变空间自回归系数和回归系数，扩展了SARAR(1,1)模型，蒙特卡洛模拟验证了极大似然估计的有限样本性质，并在投资组合优化中表现出经济优势。
Summary We propose a new class of models specifically tailored for spatiotemporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, that is, SARAR(1, 1), by exploiting the recent advancements in score‐driven (SD) models typically used in time series econometrics. In particular, we allow for time‐varying spatial autoregressive coefficients as well as time‐varying regressor coefficients and cross‐sectional standard deviations. We report an extensive Monte Carlo simulation study in order to investigate the finite‐sample properties of the maximum likelihood estimator for the new class of models as well as its flexibility in explaining a misspecified dynamic spatial dependence process. The new proposed class of models is found to be economically preferred by rational investors through an application to portfolio optimization.