Estimation of spatial panel data models with randomly missing data in the dependent variable

Wei Wang and Lung-Fei Lee

Regional Science and Urban Economics, 2013, vol. 43, issue 3, 521-538

Abstract: We suggest and compare different methods for estimating spatial autoregressive panel models with randomly missing data in the dependent variable. We start with a random effects model and then generalize the model by introducing the spatial Mundlak approach. A nonlinear least squares method is suggested and a generalized method of moments estimation is developed for the model. A two-stage least squares estimation with imputation is proposed as well. We analytically compare these estimation methods and find that the generalized nonlinear least squares, best generalized two-stage least squares with imputation, and best method of moments estimators have identical asymptotic variances. The robustness of these estimation methods against unknown heteroscedasticity is also stressed since the traditional maximum likelihood approach yields inconsistent estimates under unknown heteroscedasticity. We provide finite sample evidence through Monte Carlo experiments.

Keywords: Spatial autoregressive models; Missing data; Dependent variable; GMM estimation; Nonlinear least squares; Imputation; Mundlak approach; Unknown heteroscedasticity (search for similar items in EconPapers)
JEL-codes: C13 C21 R15 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (31)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:regeco:v:43:y:2013:i:3:p:521-538

DOI: 10.1016/j.regsciurbeco.2013.02.001

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