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Spatial Differencing: Estimation and Inference

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Abstract

Spatial differencing is a spatial data transformation pioneered by Holmes (1998) increasingly used to estimate casual effects with non-experimental data. Recently, this transformation has been widely used to deal with omitted variable bias generated by local or site-specific unobservables in a “boundary-discontinuity” design setting. However, as well known in this literature, spatial differencing makes inference problematic. Indeed, given a specific distance threshold, a sample unit may be the neighbor of a number of units on the opposite side of a specific boundary inducing correlation between all differenced observations that share a common sample unit. By recognizing that the spatial differencing transformation produces a special form of dyadic data, we show that the dyadic-robust variance matrix estimator proposed by Cameron and Miller (2014) is, in general, a better solution compared to the most commonly used estimators.

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  • Federico Belotti & Edoardo Di Porto & Gianluca Santoni, 2017. "Spatial Differencing: Estimation and Inference," CSEF Working Papers 474, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
  • Handle: RePEc:sef:csefwp:474
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    Cited by:

    1. Augusto Cerqua & Guido Pellegrini, 2020. "Local multipliers at work [Local development that money cannot buy: Italy’s Contratti di Programma]," Industrial and Corporate Change, Oxford University Press, vol. 29(4), pages 959-977.
    2. Federico Belotti & Edoardo Di Porto & Gianluca Santoni, 2018. "Spatial Differencing: Estimation and Inference," CESifo Economic Studies, CESifo, vol. 64(2), pages 241-254.

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    More about this item

    Keywords

    Spatial differencing; Boundary discontinuity; robust inference; dyadic data.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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