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Spatial Weighting Matrix Estimation through Statistical Learning: Analyzing Argentinean Salary Dynamics under Structural Breaks

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  • Quintaba Pablo Aníbal
  • Herrera Gómez Marcos

Abstract

The spatial weighting matrix plays a pivotal role in spatial econometrics and remains an active area of research. In this study, we apply recent advancements in machine learning for estimating the spatial weights matrix in econometric models. By employing LASSO strategies and incorporating geographical restrictions, we directly derive the spatial weighting matrix from the available data. This approach removes the necessity for arbitrary criteria set by researchers. As an empirical example, we explore the relationship among the salary of registered salary workers of Argentine provinces. Using monthly information between 2014 and 2022, we identify breakpoints in the wage time series and determine whether the breaks occur due to the movements within each province or due to neighboring provinces.

Suggested Citation

  • Quintaba Pablo Aníbal & Herrera Gómez Marcos, 2023. "Spatial Weighting Matrix Estimation through Statistical Learning: Analyzing Argentinean Salary Dynamics under Structural Breaks," Asociación Argentina de Economía Política: Working Papers 4688, Asociación Argentina de Economía Política.
    • Handle: RePEc:aep:anales:4688
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    References listed on IDEAS

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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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