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t-Statistic based correlation and heterogeneity robust inference

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Title: t-Statistic based correlation and heterogeneity robust inference
Authors: Ibragimov, R
Mueller, UK
Item Type: Journal Article
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.
Issue Date: 1-Oct-2010
Date of Acceptance: 1-Oct-2010
URI: http://hdl.handle.net/10044/1/67782
DOI: https://dx.doi.org/10.1198/jbes.2009.08046
ISSN: 0735-0015
Publisher: Taylor & Francis
Start Page: 453
End Page: 468
Journal / Book Title: Journal of Business and Economic Statistics
Volume: 28
Issue: 4
Copyright Statement: © 2010 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Business & Economic Statistics on 1 Oct 2010, available online: https://dx.doi.org/10.1198/jbes.2009.08046
Sponsor/Funder: National Science Foundation
Funder's Grant Number: SES-0820124
Keywords: Social Sciences
Science & Technology
Physical Sciences
Economics
Social Sciences, Mathematical Methods
Statistics & Probability
Business & Economics
Mathematical Methods In Social Sciences
Mathematics
Dependence
Fama-MacBeth method
Least favorable distribution
t-test
Variance estimation
CONSISTENT COVARIANCE-MATRIX
CROSS-SECTIONAL DEPENDENCE
STRUCTURAL-CHANGE
IN-DIFFERENCES
PANEL-DATA
HETEROSKEDASTICITY
TESTS
ESTIMATOR
VARIABLES
MODELS
01 Mathematical Sciences
14 Economics
15 Commerce, Management, Tourism And Services
Econometrics
Publication Status: Published
Online Publication Date: 2012-01-01
Appears in Collections:Imperial College Business School



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