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Spatial econometrics for misaligned data

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  • Pouliot, Guillaume Allaire

Abstract

We produce methodology for regression analysis when the geographic locations of the independent and dependent variables do not coincide, in which case we speak of misaligned data. We develop and investigate two complementary methods for regression analysis with misaligned data that circumvent the need to estimate or specify the covariance of the regression errors. We carry out a detailed reanalysis of Maccini and Yang (2009) and find economically significant quantitative differences but sustain most qualitative conclusions.

Suggested Citation

  • Pouliot, Guillaume Allaire, 2023. "Spatial econometrics for misaligned data," Journal of Econometrics, Elsevier, vol. 232(1), pages 168-190.
    • Handle: RePEc:eee:econom:v:232:y:2023:i:1:p:168-190
    DOI: 10.1016/j.jeconom.2021.04.011
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    Cited by:

    1. Millimet, Daniel L., 2024. "(Don't) Walk This Way: The Econometrics of Crosswalks," IZA Discussion Papers 17154, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    Spatial econometrics; Gaussian random fields; Large sample distributions; Kriging;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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