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

(Job Market Paper)

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.

Housing Prices Propagation: A Theory of Spatial Interactions

joint with Guillaume Chapelle, Jean-Benoît Eyméoud and Etienne Wasmer

Abstract: Price-to-rent ratios in the housing market vary a lot in time and space, and this cannot be explained solely by differences in local discount rates or rent growth differences. We study a variant of asset pricing equations for housing markets that include a price gradient in space, analogous to the transport equation in physics. The rationale for it is the existence of spatial search frictions for housing. A parametrization, backed by data, implies that a 10% increase in the price of an adjacent city raises the price of the city by approximately 0.05%. These effects cumulate over time and space and lead to a 15% increase in prices in the periphery after 30 years. More complex asset price equations such as heat diffusion equations are also derived from the search friction model and briefly discussed but second order terms are not found in the data.


Dynamics of Households’ Consumption and Housing Decisions - joint with Thierry Magnac

Housing-market Matching, Employment and Regional Mobility - joint with Alexandre Gaillard


Last update: 08-2022

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Department of Economics

University of Leuven

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