Pseudo maximum likelihood estimation of spatial autoregressive models with increasing dimension

Abhimanyu Gupta and Peter M. Robinson

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Pseudo maximum likelihood estimates are developed for higher-order spatial autoregressive models with increasingly many parameters, including models with spatial lags in the dependent variables both with and without a linear or nonlinear regression component, and regression models with spatial autoregressive disturbances. Consistency and asymptotic normality of the estimates are established. Monte Carlo experiments examine finite-sample behaviour

Keywords: Spatial autoregression; Increasingly many parameters; Consistency; Asymptotic normality; Pseudo Gaussian maximum likelihood; Finite sample performance (search for similar items in EconPapers)
JEL-codes: J1 (search for similar items in EconPapers)
Date: 2017-08-18
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Citations: View citations in EconPapers (4)

Published in Journal of Econometrics, 18, August, 2017. ISSN: 0304-4076

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Related works:
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 (2015)
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