Pseudo maximum likelihood estimation of spatial autoregressive models with increasing dimension

Abhimanyu Gupta and Peter M. Robinson

Journal of Econometrics, 2018, vol. 202, issue 1, 92-107

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: C21 C31 C36 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (19)

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Related works:
Working Paper: Pseudo maximum likelihood estimation of spatial autoregressive models with increasing dimension (2017)
Working Paper: Pseudo Maximum Likelihood Estimation of Spatial Autoregressive Models with Increasing Dimension (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:202:y:2018:i:1:p:92-107

DOI: 10.1016/j.jeconom.2017.05.019

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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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