We study the identification of binary choice models with fixed effects. ...
This paper studies inference in linear models whose parameter of interes...
We consider the setting in which a strong binary instrument is available...
Breiman challenged statisticians to think more broadly, to step into the...
The method for testing equal predictive accuracy for pairs of forecastin...
Many objects of interest can be expressed as a linear, mean square conti...
In this paper, we consider the problem of learning models with a latent
...
We propose a robust method for constructing conditionally valid predicti...
Many popular methods for building confidence intervals on causal effects...
We study a panel data model with general heterogeneous effects, where sl...
This paper proposes a new framework for estimating instrumental variable...
This paper proposes a new framework for estimating instrumental variable...
This paper studies hypothesis testing and confidence interval constructi...
We extend conformal inference to general settings that allow for time se...
This paper introduces new inference methods for counterfactual and synth...
Models with many signals, high-dimensional models, often impose structur...
We provide comments on the article "High-dimensional simultaneous infere...
This article develops a framework for testing general hypothesis in
high...
In analyzing high-dimensional models, sparsity of the model parameter is...
We propose a methodology for testing linear hypothesis in high-dimension...
In high-dimensional linear models, the sparsity assumption is typically ...