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Uniform Inference in Linear Error-in-Variables Models: Divide-and-Conquer

Tom Boot, Artūras Juodis

arXiv 11 Jan 2023 · Econometrics · publishedEconometric Reviews (2024)

arXiv:2301.04439 · PDF · DOI · OpenAlex · Extracted main text

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 non-zero. 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.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Erickson, T., Jiang, C. H., and Whited, T. M (2014) Minimum distance estimation of the errors-in-variables model using linear cumulant equations1.000133100%
2Geary, R. C (1942) Inherent relations between random variables1.00063100%
3Erickson, T. and Whited, T. M (2000) Measurement error and the relationship between investment and $q$0.81142100%
4Erickson, T. and Whited, T. M (2002) Two-step GMM estimation of the errors-in-variables model using high-order moments0.81142100%
5Erickson, T. and Whited, T. M (2012) Treating measurement error in Tobin's $q$0.73732100%
6Andrei, D., Mann, W., and Moyen, N (2019) Why did the $q$ theory of investment start working?0.64422100%
7Lewbel, A (1997) Constructing instruments for regressions with measurement error when no additional data are available, with an application to pa…0.51121100%
8Alon, N., Matias, Y., and Szegedy, M (1999) The space complexity of approximating the frequency moments0.40511100%
9Angrist, J. D. and Krueger, A. B (1995) Split-sample instrumental variables estimates of the return to schooling0.40511100%
10Banerjee, M., Durot, C., and Sen, B (2019) Divide and conquer in nonstandard problems and the super-efficiency phenomenon0.40511100%

Showing the top 10 of 30 scored citations.