Pseudo Maximum Likelihood Estimation of Spatial Autoregressive Models with Increasing Dimension
Abhimanyu Gupta and
Economics Discussion Papers from University of Essex, Department of Economics
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.
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Journal Article: Pseudo maximum likelihood estimation of spatial autoregressive models with increasing dimension (2018)
Working Paper: Pseudo maximum likelihood estimation of spatial autoregressive models with increasing dimension (2017)
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