Fixed Effects and Random Effects Estimation of Higher-Order Spatial Autoregressive Models with Spatial Autoregressive and Heteroskedastic Disturbances

Harald Badinger and Peter Egger

Department of Economics Working Papers from Vienna University of Economics and Business, Department of Economics

Abstract: This paper develops a unified framework for fixed and random effects estimation of higher-order spatial autoregressive panel data models with spatial autoregressive disturbances and heteroskedasticity of unknown form in the idiosyncratic error component. We derive the moment conditions and optimal weighting matrix without distributional assumptions for a generalized moments (GM) estimation procedure of the spatial autoregressive parameters of the disturbance process and define both a random effects and a fixed effects spatial generalized two-stage least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators and derive their joint asymptotic distribution, which is robust to heteroskedasticity of unknown form in the idiosyncratic error component. Finally, we derive a robust Hausman-test of the spatial random against the spatial fixed effects model.

Keywords: Higher-order spatial dependence; Generalized moments estimation; Heteroskedasticity; Two-stage least squares; Asymptotic statistics (search for similar items in EconPapers)
JEL-codes: C13 C21 C23 (search for similar items in EconPapers)
Date: 2014-04
New Economics Papers: this item is included in nep-ecm, nep-geo, nep-ore and nep-ure
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Related works:
Journal Article: Fixed Effects and Random Effects Estimation of Higher-order Spatial Autoregressive Models with Spatial Autoregressive and Heteroscedastic Disturbances (2015)
Working Paper: Fixed Effects and Random Effects Estimation of Higher-Order Spatial Autoregressive Models with Spatial Autoregressive and Heteroskedastic Disturbances (2014)
Working Paper: Fixed Effects and Random Effects Estimation of Higher-Order Spatial Autoregressive Models with Spatial Autoregressive and Heteroskedastic Disturbances (2014)
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