Quasi Maximum Likelihood Estimation of Spatial Models with Heterogeneous Coefficients

Michele Aquaro, Natalia Bailey and Mohammad Pesaran

No 5428, CESifo Working Paper Series from CESifo

Abstract: This paper considers spatial autoregressive panel data models and extends their analysis to the case where the spatial coefficients differ across the spatial units. It derives conditions under which the spatial coefficients are identified and develops a quasi maximum likelihood (QML) estimation procedure. Under certain regularity conditions, it is shown that the QML estimators of individual spatial coefficients are consistent and asymptotically normally distributed when both the time and cross section dimensions of the panel are large. It derives the asymptotic covariance matrix of the QML estimators allowing for the possibility of non-Gaussian error processes. Small sample properties of the proposed estimators are investigated by Monte Carlo simulations for Gaussian and non-Gaussian errors, and with spatial weight matrices of differing degree of sparseness. The simulation results are in line with the paper’s key theoretical findings and show that the QML estimators have satisfactory small sample properties for panels with moderate time dimensions and irrespective of the number of cross section units in the panel, under certain sparsity conditions on the spatial weight matrix.

Keywords: spatial panel data models; heterogeneous spatial lag; coefficients; identification; quasi maximum likelihood (QML) estimators; non-Gaussian errors (search for similar items in EconPapers)
JEL-codes: C21 C23 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (26)

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Working Paper: Quasi Maximum Likelihood Estimation of Spatial Models with Heterogeneous Coefficients (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_5428

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