Imperial College London


Business School

Professor of Finance and Econometrics



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BibTex format

author = {Ibragimov, R and Mueller, UK},
doi = {10.1198/jbes.2009.08046},
journal = {Journal of Business and Economic Statistics},
pages = {453--468},
title = {t-Statistic based correlation and heterogeneity robust inference},
url = {},
volume = {28},
year = {2010}

RIS format (EndNote, RefMan)

AB - 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.
AU - Ibragimov,R
AU - Mueller,UK
DO - 10.1198/jbes.2009.08046
EP - 468
PY - 2010///
SN - 0735-0015
SP - 453
TI - t-Statistic based correlation and heterogeneity robust inference
T2 - Journal of Business and Economic Statistics
UR -
UR -
UR -
VL - 28
ER -