This paper introduces a Spatial Vector Autoregressive Moving Average (SVARMA) model in which multiple cross-sectional time series are modeled as multivariate, possibly fat-tailed, spatial autoregressive ARMA processes. The estimation requires specifying the cross-sectional spillover channels...

Background It is a well-known phenomenon that nonlinearities that are inherent in the relationship among economic variables negatively affect the commonly used estimators in the econometric models. The nonlinearities cause an instability of the estimated parameters that, in particular, are...

In this paper, we study the spatial dynamic panel data models with high-order time-varying endogenous weights matrices. The quasi-maximum likelihood (QML) estimator is inconsistent under heteroskedastic errors and would be computationally complicated due to the evaluation of the Jacobian...

In this paper, we study the spatial dynamic panel data models with high-order time-varying endogenous weights matrices. To estimate the model, we propose a generalized method of moments (GMM) estimator. Compared to the traditional quasi-maximum likelihood (QML) estimator, GMM estimators can...