Don't (fully) exclude me, it's not necessary! Identification with semi-IVs (SSRN) (arXiv)

Abstract: This paper proposes a novel approach to identify models with a discrete endogenous variable, that I study in the general context of nonseparable models with continuous potential outcomes. I show that nonparametric identification of the potential outcome and selection equations, and thus of the individual treatment effects, can be obtained with semi-instrumental variables (semi-IVs), which are relevant but only partially excluded from the potential outcomes, i.e., excluded from one or more potential outcome equations, but not necessarily all. This contrasts with the full exclusion restriction imposed on standard instrumental variables (IVs), which is stronger than necessary for identification: IVs are only a special case of valid semi-IVs. In practice, there is a trade-off between imposing stronger exclusion restrictions, and finding semi-IVs with a larger support and stronger relevance assumptions. Since, in empirical work, the main obstacle for finding a valid IV is often the full exclusion restriction, tackling the endogeneity problem with semi-IVs instead should be an attractive alternative.

Discrete-Continuous Dynamic Choice Models: Identification and Conditional Choice Probability Estimation (SSRN) 

Abstract: This paper develops a general framework for models and games, static or dynamic, in which individuals simultaneously make both discrete and continuous choices. The framework incorporates a wide range of unobserved heterogeneity. I show that such models are nonparametrically identified. Based on constructive identification arguments, I build a novel two-step estimation method in the lineage of Hotz and Miller (1993) and Arcidiacono and Miller (2011) but extended to simultaneous discrete-continuous choice. In the first step, I recover the (type-dependent) optimal choices with an expectation-maximization algorithm. In the second step, I estimate the primitives of the model taking the estimated optimal choices as given. The method is especially attractive for complex dynamic models because it significantly reduces the computational burden associated with their estimation compared to alternative full solution methods.

Imperfect information, Learning and Housing Market Dynamics (pdf)
Best Phd student paper award IAAE 2018

Abstract: This paper examines the decision problem of a homeowner who maximizes her expected profit from the sale of her property when market conditions are uncertain. Using a large dataset of real estate transactions in Pennsylvania between 2011 and 2014, I verify several stylized facts about the housing market. I develop a dynamic search model of the home-selling problem in which the homeowner learns about demand in a Bayesian way. I estimate the model and find that learning, especially the downward adjustment of the beliefs of sellers facing low demand, explains some of the key features of the housing data, such as the decreasing list price overtime and time on the market. By comparing with a perfect information benchmark, I derive an unexpected result: the value of information is not always positive. Indeed, an imperfectly informed seller facing low demand can obtain a better outcome than her perfectly informed counterpart thanks to a delusively stronger bargaining position.