Monsieur Christophe-Alain Bruneel-Zupanc soutiendra sa thèse de doctorat en Sciences économiques le 06 Juillet 2021 à 15h30
Visioconférence (TSE)
Sur le sujet : « Essays in Structural Econometrics »
Directeur de thèse : Thierry Magnac
Le jury se compose comme suit :
- Monsieur Arnaud Maurel, Professeur Associé, Duke University
- Monsieur Robert A. Miller, Professeur d’économie, Carnegie Mellon University
- Monsieur Franck Verboven, Professeur d’économie, KU Leuven
- Monsieur Pierre Dubois, Professeur d’économie, UT1 Capitole - TSE
- Monsieur Olivier De Groote, Professeur assistant, UT1 Capitole - TSE
- Monsieur Thierry Magnac, Professeur d’économie, UT1 Capitole - TSE
Résumé de thèse :
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 non-parametrically 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 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 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.
In the third chapter, 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 downpayments.
