  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.
