Discrete-continuous dynamic choice models: identification and conditional choice probability estimation

This paper develops a general framework for models, static or dynamic, in which agents simultaneously make both discrete and continuous choices. I show that such models are nonparametrically identified. Based on the constructive identification arguments, I build a novel two-step estimation method in the lineage of Hotz and Miller (1993) but extended to discrete and continuous choice models. The method is especially attractive for complex dynamic models because it significantly reduces the computational burden associated with their estimation. To illustrate my new method, I estimate a dynamic model of female labor supply and consumption

Imperfect Information, Learning and Housing Market Dynamics

This paper examines the decision problem of a homeowner who maximizes her expected profitfrom the sale of her property when market conditions are uncertain. Using a large dataset of realestate transactions in Pennsylvania between 2011 and 2014, I verify several stylized facts aboutthe housing market. I develop a dynamic search model of the home-selling problem in which thehomeowner 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 thekey 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 ofinformation is not always positive. Indeed, an imperfectly informed seller facing low demand canobtain a better outcome than her perfectly informed counterpart thanks to a delusively strongerbargaining position

Imperfect Information, Learning and Housing Market Dynamics

This paper examines the decision problem of a homeowner who maximizes her expected profitfrom the sale of her property when market conditions are uncertain. Using a large dataset of realestate transactions in Pennsylvania between 2011 and 2014, I verify several stylized facts aboutthe housing market. I develop a dynamic search model of the home-selling problem in which thehomeowner 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 thekey 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 ofinformation is not always positive. Indeed, an imperfectly informed seller facing low demand canobtain a better outcome than her perfectly informed counterpart thanks to a delusively strongerbargaining position

Discrete-continuous dynamic choice models: identification and conditional choice probability estimation

This paper develops a general framework for models, static or dynamic, in which agents simultaneously make both discrete and continuous choices. I show that such models are nonparametrically identified. Based on the constructive identification arguments, I build a novel two-step estimation method in the lineage of Hotz and Miller (1993) but extended to discrete and continuous choice models. The method is especially attractive for complex dynamic models because it significantly reduces the computational burden associated with their estimation. To illustrate my new method, I estimate a dynamic model of female labor supply and consumption

Essays in Structural Econometrics

Le résumé en français n'a pas été communiqué par l'auteur.The first chapter develops a general framework for models, static or dynamic, in which agents simultaneously make both discrete and continuous choices. I show that such models are nonparametrically identified. Based on the constructive identification arguments, I build a novel twostep estimation method in the lineage of Hotz and Miller (1993) but extended to discrete and continuous choice models. The method is especially attractive for complex dynamic models because it significantly reduces the computational burden associated with their estimation. To illustrate my new method, I estimate a dynamic micro-model of female labor supply and consumption. The method is also illustrated in the third chapter of the thesis. In the second chapter, I build a dynamic search model to examine 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 2011and 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. In the third chapter, joint work with Thierry Magnac, we estimate a dynamic discrete and continuous choices model of households’ decisions regarding their consumption, housing tenure and housing services over the life-cycle. We use non-parametric identification arguments as in the first chapter to formulate an empirical strategy in two steps that (1) estimates discrete choice probabilities and continuous choices distribution summaries to be used in (2) Bellman and Euler equations that estimate the structural parameters. Specific modelling strategies are adopted because of unfrequent mobility due to housing transaction costs. Counterfactuals that can be evaluated are related to those transaction costs as well as of prudential policies such as down payments