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Pseudo Maximum Likelihood Estimation of Spatial Autoregressive Models with Increasing Dimension

Abhimanyu Gupta and Pm Robinson

Economics Discussion Papers from University of Essex, Department of Economics

Abstract: Pseudo maximum likelihood estimates are developed for higher-order spatial autoregres- sive models with increasingly many parameters, including models with spatial lags in the dependent variables and regression models with spatial autoregressive disturbances. We consider models with and without a linear or nonlinear regression component. Sufficient conditions for consistency and asymptotic normality are provided, the results varying ac- cording to whether the number of neighbours of a particular unit diverges or is bounded. Monte Carlo experiments examine nite-sample behaviour.

Date: 2015-10-22
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Related works:
Journal Article: Pseudo maximum likelihood estimation of spatial autoregressive models with increasing dimension (2018) Downloads
Working Paper: Pseudo maximum likelihood estimation of spatial autoregressive models with increasing dimension (2017) Downloads
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