Estimation of Partially Linear Spatial Autoregressive Models with Autoregressive Disturbances

Takaki Sato

No 104, DSSR Discussion Papers from Graduate School of Economics and Management, Tohoku University

Abstract: This study considers semiparametric partially linear spatial autoregressive models with autoregressive disturbances that contain an unspecified nonparametric component and allow for spatial lags in both the dependent variables and disturbances. Having the nonparametric function approximated by basis functions, we propose a three-step estimation procedure for the proposed model. We also establish the consistency and asymptotic normality of the proposed estimators. Then, the finite sample performances of the proposed estimators are examined using Monte Carlo simulations. As an empirical application, we use the proposed model and estimation method to analyze Boston housing price data to evaluate the effect of air pollution on the value of owner-occupied homes.

Pages: 23 pages
Date: 2019-10
New Economics Papers: this item is included in nep-ecm and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:toh:dssraa:104

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