Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/284146 
Year of Publication: 
2023
Series/Report no.: 
cemmap working paper No. CWP22/23
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
This paper proposes a powerful alternative to the t-test of the null hypothesis that a coefficient in linear regression is equal to zero when a regressor is mismeasured. We assume there are two contaminated measurements of the regressor of interest. We allow the two measurement errors to be nonclassical in the sense that they may both be correlated with the true regressor, they may be correlated with each other, and we do not require any location normalizations on the measurement errors. We propose a new maximal t-statistic that is formed from the regression of the outcome onto a maximally weighted linear combination of the two measurements. Critical values of the test are easily computed via a multiplier bootstrap. In simulations, we show that this new test can be significantly more powerful than t-statistics based on OLS or IV estimates. Finally, we apply the proposed test to a study of returns to education based on twins data from the UK. With our maximal t-test, we can discover statistically significant returns to education when standard t-tests do not.
Subjects: 
Statistical test
Statistical error
Regression analysis
Simulation
Educational returns
Great Britain
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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