This article reviews recent progress in high-dimensional bootstrap. We first review high-
dimensional central limit theorems for distributions of sample mean vectors over the …

This chapter reviews the microeconometrics literature on partial identification, focusing on
the developments of the last thirty years. The topics presented illustrate that the available …

Exporting firms often enter foreign markets that are similar to their previous export
destinations. We develop a dynamic model in which a firm's exports in a market may depend …

Decision makers, such as doctors, judges, and managers, make consequential choices
based on predictions of unknown outcomes. Do these decision makers make systematic …

We propose a novel technique to boost the power of testing a high‐dimensional vector H: θ=
0 against sparse alternatives where the null hypothesis is violated by only a few …

Improved central limit theorem and bootstrap approximations in high dimensions Page 1 The
Annals of Statistics 2022, Vol. 50, No. 5, 2562–2586 https://doi.org/10.1214/22-AOS2193 © …

Data snooping is a major concern in empirical asset pricing. We develop a new framework
to rigorously perform multiple hypothesis testing in linear asset pricing models, while limiting …

This paper illustrates how one can deduce preference from observed choices when
attention is both limited and random. We introduce a random attention model where we …

Gaussian approximation for high dimensional time series Page 1 The Annals of Statistics 2017,
Vol. 45, No. 5, 1895–1919 DOI: 10.1214/16-AOS1512 © Institute of Mathematical Statistics …

This paper develops a systematic theory for high-dimensional linear models with dependent
errors and/or dependent covariates. To study properties of estimates of the regression …