Finite Sample Bias of the QMLE in Spatial Autoregressive Models

We investigate the finite-sample bias of the quasi-maximum likelihood estimator (QMLE) in spatial autoregressive models with possible exogenous regressors. We derive the approximate bias result of the QMLE in terms of model parameters and also the moments (up to order 4) of the error distribution, and thus a feasible bias-correction procedure is directly applicable. In some special cases, the analytical bias result can be significantly simplified. Our Monte Carlo results demonstrate that the feasible bias-correction procedure works remarkably well

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Year of publication:	2016
Authors:	Bao, Yong
Publisher:	[2016]: [S.l.] : SSRN
Subject:	Autokorrelation \| Autocorrelation \| Systematischer Fehler \| Bias \| Maximum-Likelihood-Schätzung \| Maximum likelihood estimation \| Schätztheorie \| Estimation theory \| Räumliche Interaktion \| Spatial interaction \| Stichprobenerhebung \| Sampling \| Monte-Carlo-Simulation \| Monte Carlo simulation
Description of contents:	Abstract [papers.ssrn.com]