acreg: Arbitrary correlation regression

Fabrizio Colella, Rafael Lalive, Seyhun Orcan Sakalli () and Mathias Thoenig
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Seyhun Orcan Sakalli: King’s College London

Stata Journal, 2023, vol. 23, issue 1, 119-147

Abstract: We present acreg, a new command that implements the arbitrary clustering correction of standard errors proposed in Colella et al. (2019, IZA dis- cussion paper 12584). Arbitrary here refers to the way observational units are correlated with each other: we impose no restrictions so that our approach can be used with a wide range of data. The command accommodates both cross-sectional and panel databases and allows the estimation of ordinary least-squares and two- stage least-squares coefficients, correcting standard errors in three environments: in a spatial setting using units’ coordinates or distance between units, in a network setting starting from the adjacency matrix, and in a multiway clustering frame- work taking multiple clustering variables as input. Distance and time cutoffs can be specified by the user, and linear decays in time and space are also optional.

Keywords: acreg; spatial correlation; time correlation; inference; spatial data; network data (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1177/1536867X231162031

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