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

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  • Alejandro Izaguirre

    (Universidad de San Andrés, Buenos Aires, Argentina Facultad de Ciencias Económicas y Estadística, Universidad Nacional de Rosario)

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.

Suggested Citation

  • Alejandro Izaguirre, 2021. "Estimation of Spatial Lag Model Under Random Missing Data in the Dependent Variable. Two Stage Estimator with Imputation," Revista Economía, Fondo Editorial - Pontificia Universidad Católica del Perú, vol. 44(87), pages 1-19.
    • Handle: RePEc:pcp:pucrev:y:2021:i:87:p:1-19
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    File URL: https://revistas.pucp.edu.pe/index.php/economia/article/view/23710/22645
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    More about this item

    Keywords

    Random missing data; Two stage estimators; Imputation; Spatial lag model;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • R5 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis

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