Time series data are widely used to explore causal relationships, typically in a regression framework with lagged dependent variables. Regression-based causality tests rely on an array of functional form and distributional assumptions for valid causal inference. This paper develops a...

This paper estimates the dynamic returns to job training. We posit a dynamic-discrete choice model of sequential training participation, where choices and earnings depend on observed and unobserved characteristics.We define treatment effects, including policy relevant parameters, and link them...

We consider nonparametric identification and estimation in a nonseparable model where a continuous regressor of interest is a known, deterministic, but kinked function of an observed assignment variable. This design arises in many institutional settings where a policy variable (such as weekly...