Spatial Differencing: Estimation and Inference

Federico Belotti, Edoardo Di Porto and Gianluca Santoni

CESifo Economic Studies, 2018, vol. 64, issue 2, 241-254

Abstract: Spatial differencing (SD) is a spatial data transformation pioneered by Holmes (1998) increasingly used to estimate causal 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 is well known in this literature, SD 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 SD 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.

Keywords: spatial differencing; boundary discontinuity; robust inference; dyadic data (search for similar items in EconPapers)
JEL-codes: C12 C21 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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Working Paper: Spatial Differencing: Estimation and Inference (2017)
Working Paper: Spatial Differencing: Estimation and Inference (2017)
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