Výsledky vyhledávání

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In theory, two-stage least squares (TSLS) identifies a weighted average of covariate-specific local average treatment effects (LATEs) from a saturated specification without making parametric assumptions on how available covariates enter the model. In

Externí odkaz: http://arxiv.org/abs/2402.12607

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Identification- and many instrument-robust inference via invariant moment conditions

Autor: Boot, Tom, Ligtenberg, Johannes W.

Identification-robust hypothesis tests are commonly based on the continuous updating objective function or its score. When the number of moment conditions grows proportionally with the sample size, the large-dimensional weighting matrix prohibits the

Externí odkaz: http://arxiv.org/abs/2303.07822

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

Autor: Boot, Tom, Juodis, Artūras

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

Externí odkaz: http://arxiv.org/abs/2301.04439

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Unbiased estimation of the OLS covariance matrix when the errors are clustered

Autor: Boot, Tom, Niccodemi, Gianmaria, Wansbeek, Tom

When data are clustered, common practice has become to do OLS and use an estimator of the covariance matrix of the OLS estimator that comes close to unbiasedness. In this paper we derive an estimator that is unbiased when the random-effects model hol

Externí odkaz: http://arxiv.org/abs/2206.09644

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Akademický článek

Where’s Waldo? A framework for quantifying the privacy-utility trade-off in marketing applications

Autor: Ponte, Gilian R., Wieringa, Jaap E., Boot, Tom, Verhoef, Peter C.

Publikováno v: In International Journal of Research in Marketing September 2024 41(3):529-546

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Akademický článek

Joint inference based on Stein-type averaging estimators in the linear regression model

Autor: Boot, Tom

Publikováno v: In Journal of Econometrics August 2023 235(2):1542-1563

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Scalable simultaneous inference in high-dimensional linear regression models

Autor: Boot, Tom, Nibbering, Didier

The computational complexity of simultaneous inference methods in high-dimensional linear regression models quickly increases with the number variables. This paper proposes a computationally efficient method based on the Moore-Penrose pseudoinverse.

Externí odkaz: http://arxiv.org/abs/1703.03282

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