T -statistic based correlation and heterogeneity robust inference

Rustam Ibragimov, Ulrich K. Müller

Research output: Contribution to journalArticle

93 Scopus citations

Abstract

We develop a general approach to robust inference about a scalar parameter of interest when the data is potentially heterogeneous and correlated in a largely unknown way. The key ingredient is the following result of Bakirov and Székely (2005) concerning the small sample properties of the standard t-test: For a significance level of 5% or lower, the t-test remains conservative for underlying observations that are independent and Gaussian with heterogenous variances. One might thus conduct robust large sample inference as follows: partition the data into q ≥ 2 groups, estimate the model for each group, and conduct a standard t-test with the resulting q parameter estimators of interest. This results in valid and in some sense efficient inference when the groups are chosen in a way that ensures the parameter estimators to be asymptotically independent, unbiased and Gaussian of possibly different variances. We provide examples of how to apply this approach to time series, panel, clustered and spatially correlated data.

Original languageEnglish (US)
Pages (from-to)453-468
Number of pages16
JournalJournal of Business and Economic Statistics
Volume28
Issue number4
DOIs
StatePublished - Oct 2010

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

Keywords

  • Dependence
  • Fama-MacBeth method
  • Least favorable distribution
  • T-test
  • Variance estimation

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