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
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)
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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
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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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