Abstract While the Paris Agreement and 2030 Agenda for Sustainable Development are the
two most important global governance agendas, in practice they have been implemented in …

This chapter presents key concepts and theoretical results for analyzing estimation and
inference in high-dimensional models. High-dimensional models are characterized by …

In this paper, we estimate impulse responses by local projections in high-dimensional
settings. We use the desparsified (de-biased) lasso to estimate the high-dimensional local …

We investigate the high-dimensional linear regression problem in the presence of noise that
is correlated with Gaussian covariates. This type of correlation, known as endogeneity in …

Much of the theory for the lasso in the linear model Y= X β*+ ε hinges on the quantity 2∥ X
Τε∥∞/n, which we call the lasso's effective noise. Among other things, the effective noise …

This study proposes novel estimation and inference approaches for heterogeneous local
treatment effects using high-dimensional covariates and observational data without a strong …

Linear regression relates predictor variables and outcome variables, such as gene copy
numbers and the level of a biomarker. The assumed linearity of the relationships makes the …

Inferring causal relationships or related associations from observational data can be
invalidated by the existence of hidden confounding. We focus on a high-dimensional linear …

This paper proposes a desparsified GMM estimator for estimating high-dimensional
regression models allowing for, but not requiring, many more endogenous regressors than …

Through this paper, we examined the relationship between macroeconomic factors and new
entrepreneurial firms in France during the 2000–2020 period; this relationship included …