Uniform inference in linear error-in-variables models: Divide-and-conquer

Tom Boot and Artūras Juodis

Econometric Reviews, 2025, vol. 44, issue 3, 335-355

Abstract: It is customary to estimate error-in-variables models using higher-order moments of observables. This moments-based estimator is consistent only when the coefficient of the latent regressor is assumed to be nonzero. We develop a new estimator based on the divide-and-conquer principle that is consistent for any value of the coefficient of the latent regressor. In an application on the relation between investment, (mismeasured) Tobin’s q and cash flow, we find time periods in which the effect of Tobin’s q is not statistically different from zero. The implausibly large higher-order moment estimates in these periods disappear when using the proposed estimator.

Date: 2025
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DOI: 10.1080/07474938.2024.2417166

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