Robust two-pass cross-sectional regressions: A minimum distance approach

Seung Ahn, Christopher Gadarowski, M. Fabricio Perez

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


We examine the asymptotic and finite-sample properties of the two-pass (TP) cross-sectional regressions estimators when factors and asset returns are conditionally heteroskedastic and/or autocorrelated. Using a minimum distance approach, we derive the heteroskedasticity- and/or autocorrelation-consistent (HAC) standard errors and the optimal TP estimator. A HAC model specification test statistic is also derived. Our Monte Carlo simulation results reveal the importance of controlling for autocorrelation. The HAC standard errors produce the most reliable inferences under autocorrelation. The HAC specification test is a viable alternative if the number of asset returns is small and the number of time-series observations is large.

Original languageEnglish (US)
Article numbernbs006
Pages (from-to)669-701
Number of pages33
JournalJournal of Financial Econometrics
Issue number4
StatePublished - Sep 2012


  • Cross-sectional regression
  • Factor model
  • Fama and MacBeth
  • Minimum distance
  • Robust standard errors
  • Two-pass

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics


Dive into the research topics of 'Robust two-pass cross-sectional regressions: A minimum distance approach'. Together they form a unique fingerprint.

Cite this