1 citations · 1 across the 7 of their papers we have counts for
8 papers
Testing for peer effects in short panels without identifying the network structure
Tom Boot, Artūras Juodis
We propose an instrument-based test for peer effects in short-T panels when the underlying network is unknown. The test avoids estimating the network by aggregating pairwise compar…
Transformer-based CoVaR: Systemic Risk in Textual Information
Junyu Chen, Tom Boot, Lingwei Kong +1
Conditional Value-at-Risk (CoVaR) quantifies systemic financial risk by measuring the loss quantile of one asset, conditional on another asset experiencing distress. We develop a T…
Diffusion index forecasts under weaker loadings: PCA, ridge regression, and random projections
Tom Boot, Bart Keijsers
We study the accuracy of forecasts in the diffusion index forecast model with possibly weak loadings. The default option to construct forecasts is to estimate the factors through p…
Inference on LATEs with covariates
Tom Boot, Didier Nibbering
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…
Identification- and many moment-robust inference via invariant moment conditions
Tom Boot, Johannes W. Ligtenberg
Identification-robust hypothesis tests are commonly based on the continuous updating GMM objective function. When the number of moment conditions grows proportionally with the samp…
Uniform Inference in Linear Error-in-Variables Models: Divide-and-Conquer
Tom Boot, Artūras Juodis
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…
Unbiased estimation of the OLS covariance matrix when the errors are clustered
Tom Boot, Gianmaria Niccodemi, Tom Wansbeek
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…
Scalable simultaneous inference in high-dimensional linear regression models
Tom Boot, Didier Nibbering
The computational complexity of simultaneous inference methods in high-dimensional linear regression models quickly increases with the number variables. This paper proposes a compu…