The estimation of dynamic causal effects is one of the crucial challenges of econometrics. In the macroeconomic literature, dynamic causal effects are conceived as the effect, over time, of an intervention that propagates through the economy. This is usually modeled via impulse response analysis...

Modeling dynamic systems with linear parametric models usually suffer limitation which affects forecasting performance and policy implications. This paper advances a non-parametric autoregressive distributed lag model that employs a Bayesian additive regression tree (BART). The performance of...

I consider a setting where judges differ in the quality of their decisions and sort unobservable types into two groups. I identify properties of evidence generation processes which lead to violations of monotonicity, as defined in instrumental variable designs exploiting variations in judge...

We propose a general protocol for calibration and validation of complex simulation models by an approach based on discovery and comparison of causal structures. The key idea is that configurations of parameters of a given theoretical model are selected by minimizing a distance index between two...

More than ten percent of Americans with recent work experience say they will continue social distancing after the COVID-19 pandemic ends, and another 45 percent will do so in limited ways. We uncover this Long Social Distancing phenomenon in our monthly Survey of Working Arrangements and...

We present experimental evidence that enabling access to universal early child care for families with lower socioeconomic status (SES) increases maternal labor supply. Our intervention provides families with customized help for child care applications, resulting in a large increase in enrollment...