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acreg: Arbitrary correlation regression

Author

Listed:
  • Fabrizio Colella

    (University College London)

  • Rafael Lalive

    (HEC Lausanne)

  • Seyhun Orcan Sakalli

    (King’s College London)

  • Mathias Thoenig

    (HEC Lausanne)

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.

Suggested Citation

  • Fabrizio Colella & Rafael Lalive & Seyhun Orcan Sakalli & Mathias Thoenig, 2023. "acreg: Arbitrary correlation regression," Stata Journal, StataCorp LP, vol. 23(1), pages 119-147, March.
    • Handle: RePEc:tsj:stataj:v:23:y:2023:i:1:p:119-147
    DOI: 10.1177/1536867X231162031
    Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj23-1/st0703/
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