Spatial Differencing: Estimation and Inference

Federico Belotti, Edoardo Di Porto and Gianluca Santoni

Working Papers from CEPII research center

Abstract: Spatial differencing 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 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.

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: 2017-06
New Economics Papers: this item is included in nep-geo and nep-ure
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Spatial Differencing: Estimation and Inference (2018)
Working Paper: Spatial Differencing: Estimation and Inference (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:cii:cepidt:2017-10

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