Dynamic spatial autoregressive models with autoregressive and heteroskedastic disturbances. (22nd February 2017)

• Record Type: Journal Article
• Authors: Catania, Leopoldo; Billé, Anna Gloria
• Is Part Of: Journal of applied econometrics. Volume 32:Number 6(2017)
• Page Start: 1178
• Page End: 1196
• Publication Date: 2017-02-22
• Subjects: Econometrics -- Periodicals; 330.015195
• DOI: 10.1002/jae.2565
• Languages: English
• ISSNs: 0883-7252

Abstract

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.