Estimation of Spatial Lag Model Under Random Missing Data in the Dependent Variable. Two Stage Estimator with Imputation

Alejandro Izaguirre
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Alejandro Izaguirre: Universidad de San Andrés, Buenos Aires, Argentina Facultad de Ciencias Económicas y Estadística, Universidad Nacional de Rosario

Revista Economía, 2021, vol. 44, issue 87, 1-19

Abstract: The main goal of this article is to propose estimators for the Spatial Lag Model (SLM) under miss-ing data context. We present three alternatives estimators for the SLM based on Two Stage LeastSquares estimation methodology. The estimators are efficient within their type and consistentunder random missing data in the dependent variable. Unlike the IBG2SLS estimator presentedin Wang and Lee (2013) which impute all missing data we only impute missing data in the spatiallag. Our first proposal is an alternative version of the IBG2SLS estimator, the second one is basedon an approximation to the optimal instruments matrix and the third one is an alternative√n-equivalent to the first. Thorough a Monte Carlo simulation we assess the estimators performanceunder finite samples. Results show a good performance for all estimators, moreover, results arequite similar to the IBG2SLS estimator suggesting that a complete imputation (as IBG2SLS does) does not add information.

Keywords: Random missing data; Two stage estimators; Imputation; Spatial lag model (search for similar items in EconPapers)
JEL-codes: C13 C21 R5 (search for similar items in EconPapers)
Date: 2021
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