Dynamic Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances

Leopoldo Catania () and Anna Gloria Billé

No 375, CEIS Research Paper from Tor Vergata University, CEIS

Abstract: We propose a new class of models specifically tailored for spatio{temporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, i.e. 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 exibility in explaining several dynamic spatial dependence processes. The new proposed class of models are found to be economically preferred by rational investors through an application in portfolio optimization.

Keywords: SARAR; time varying parameters; spatio{temporal data; score driven models (search for similar items in EconPapers)
Pages: 36 pages
Date: 2016-03-31, Revised 2016-03-31
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ure
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Working Paper: Dynamic Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances (2023)
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