We study consumer spending dynamics during one such time, the first infection wave of the COVID-19 pandemic, using household scanner data covering fast-moving consumer goods in the United Kingdom.

We evaluate an intervention targeting early life nutrition and well-being for households in extreme poverty in Northern Nigeria. The intervention leads to large and sustained improvements in children’s anthropometric and health outcomes, including an 8% reduction in stunting four years post-intervention.

This paper analyzes Structural Vector Autoregressions (SVARs) where identification of structural parameters holds locally but not globally.

We propose a new simulation-based estimation method, adversarial estimation, for structural models.

We investigate state‐dependent effects of fiscal multipliers and allow for endogenous sample splitting to determine whether the U.S. economy is in a slack state.

This paper builds on Bonhomme (2012) to develop a method to systematically construct moment conditions for dynamic panel data logit models with fixed effects.

We propose a framework for estimation and inference when the model may be misspecified.

We study the effects of counterfactual teacher-to-classroom assignments on average student achievement in elementary and middle schools in the US.

Manufacturers frequently pay fees to supermarkets when they temporarily reduce prices of their products.

In an important class of econometric problems, researchers select a target parameter by maximizing the Euclidean norm of a data-dependent vector.

Uncertainty about the choice of identifying assumptions is common in causal studies, but is often ignored in empirical practice.

In this paper, we estimate the time-varying COVID-19 contact rate of a Susceptible-Infected-Recovered (SIR) model.