Spatial econometrics for misaligned data

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.(c) 2021 Published by Elsevier B.V.

Automated summary

This study presents methodology for regression analysis of misaligned data, where the independent and dependent variables do not coincide geographically. Two complementary methods are developed and investigated to avoid the need for covariance estimation or specification. A detailed reanalysis of Maccini and Yang (2009) reveals significant quantitative differences but largely sustains qualitative conclusions.