Identification and QML estimation of multivariate and simultaneous equations spatial autoregressive models

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• 作者：Kai Yang^a ; ^b ; ^{yang.kai@mail.shufe.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Lung-fei Lee^c ; ^{lee.1777@osu.edu" class="auth_mail" title="E-mail the corresponding author}
• 关键词：C13 ; C30 ; C31
• 刊名：Journal of Econometrics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：196
• 期：1
• 页码：196-214
• 全文大小：609 K

文摘

This paper investigates a simultaneous equations spatial autoregressive model which incorporates simultaneity effects, own-variable spatial lags and cross-variable spatial lags as explanatory variables, and allows for correlation between disturbances across equations. In exposition, we also discuss a multivariate spatial autoregressive model that can be treated as a reduced form of the simultaneous equations model. We study parameter spaces, parameter identification, asymptotic properties of the quasi-maximum likelihood estimation, and computational issues. Monte Carlo experiments illustrate the advantages of the QML, broader applicability and efficiency, compared to instrumental variables based estimation methods in the existing literature.