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The generalized spatial random effects model in R

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  • Giovanni Millo

    (DEAMS, University of Trieste)

Abstract

We describe a user-friendly, production quality R implementation of the maximum likelihood estimator of the generalized spatial random effects (GSRE) model of Baltagi, Egger and Pfaffermayr within the well known ’splm’ package for spatial panel econometrics. We extend the maximum likelihood estimator for the GSRE to including a spatial lag of the dependent variable (SAR), and we discuss the theoretical and computational approach. This is the first implementation of the SAR+GSRE, and the second of the original GSRE. Until recently only estimators restricting the spatial structure of individual effects in an arbitrary way have been available and widely employed in applied practice. We present results from the SAR+GSRE and the restricted estimators side by side, drawing on some well-known examples from the spatial econometrics literature. The potential biases from imposing inappropriate restrictions to the spatial error process and/or from omitting the SAR term are illustrated by simulation.

Suggested Citation

  • Giovanni Millo, 2022. "The generalized spatial random effects model in R," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-18, December.
    • Handle: RePEc:spr:jospat:v:3:y:2022:i:1:d:10.1007_s43071-022-00024-9
    DOI: 10.1007/s43071-022-00024-9
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    More about this item

    Keywords

    Generalized spatial panel; Random effects; Maximum likelihood; R;
    All these keywords.

    JEL classification:

    • R1 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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