Abstract
In the field of spatial economics, choosing the right variables for spatial error models (SEM) with missing data is of utmost importance. Many excellent approaches are proposed to select variables for regression models with missing data. However, little literature addresses this problem in the SEM model. To address this issue, we have developed an observed-data penalized quasi-maximum likelihood estimation method called OPQMLE, which simultaneously performs variable selection and parameter estimation in the presence of a missing response. This method employs the Smoothly Clipped Absolute Deviation (SCAD) penalty to select variables for SEM models. Under certain assumptions, we have established the method's theoretical properties, including consistency and asymptotic normality. Furthermore, we have provided an improved expectation-maximization algorithm for optimizing the penalized quasi-likelihood function. We have conducted a simulation and real data analysis to evaluate the proposed method's performance.
Published Version
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