Colella, Fabrizio;
Lalive, Rafael;
Sakalli, Seyhun Orcan;
Thoenig, Mathias;
(2023)
acreg: Arbitrary correlation regression.
The Stata Journal
, 23
(1)
pp. 119-147.
10.1177/1536867X231162031.
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Abstract
We present acreg, a new command that implements the arbitrary clustering correction of standard errors proposed in Colella et al. (2019, IZA discussion 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 framework 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.
| Type: | Article |
|---|---|
| Title: | acreg: Arbitrary correlation regression |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1177/1536867X231162031 |
| Publisher version: | https://doi.org/10.1177/1536867X231162031 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | acreg, inference, arbitrary correlation, geospatial data, network data |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10171200 |
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