Mixture models are versatile tools that are used extensively in many fields, including operations, marketing, and econometrics. The main challenge in estimating mixture models is that the mixing distribution is often unknown and imposing apriori parametric assumptions can lead to model...

The standard regression discontinuity (RD) design deals with a binary treatment. Manyempirical applications of RD designs involve continuous treatments. This paper establishesidentification and robust bias-corrected inference for such RD design. Causal identificationis achieved by utilizing any...

I propose a nonparametric structural estimator for the distribution of liquidity needs in a version of the Diamond and Dybvig (1983) model when only the aggregate level of withdrawals is observed. The model is an extension of Peck and Shell (2003) with a continuum of depositors. I show how the...

We revisit the classical problem of price elasticity estimation from a causal perspective. When the price is perceived as a continuous but endogenous treatment, the flexible estimation of price elasticity can be turned into the estimation of heterogeneous treatment effects. To this end, we...

This paper studies a dynamic discrete choice model including an unobservable state, which evolves by a hidden action of agents. First, this paper shows new nonparametric identification results for observable and unobservable conditional choice probabilities (CCPs) and an initial type probability...

This paper analyzes the problem of weak instruments on identification, estimation, and inference in a simple nonparametric model of a triangular system. The paper derives a necessary and sufficient rank condition for identification, based on which weak identification is established. Then...