acreg: Arbitrary correlation regression
Fabrizio Colella, Rafael Lalive, Seyhun Orcan Sakalli, Mathias Thoenig
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.