Essays on discrete choice models with fixed effects

• Autores: Mian Huang
• Directores de la Tesis: Jesús M. Carro (dir. tes.)
• Lectura: En la Universidad Carlos III de Madrid ( España ) en 2015
• Idioma: inglés
• Tribunal Calificador de la Tesis: Pedro Mira (presid.), Ricardo Mora Villarrubia (secret.), Pedro Albarrán Pérez (voc.)
• Materias:
  • Ciencias económicas
    • Economía general
• Enlaces
  • Tesis en acceso abierto en: e-Archivo
• Resumen

  • Fixed effects in discrete choice models has been a challenge to econometricians from its existence. These unobservable heterogeneities are so important since their impacts can be seen clearly from the behavior of agents being studied. This has been consolidated by lots of studies and simulations including mine. However their existence prevent us from identifying models without restrictive assumptions about them. It is also hard to get rid of fixed effects since they enter the model not in a linear additive way and the outcomes are not continuous, therefore extant difference methods do not apply to discrete choice models with fixed effects. To have flexible specification on the fixed effects, it seems that partial identification is more practicable. There do exists some idea about set identification for discrete choice models and even some estimation methods were proposed for logistic-alike discrete choice models, whose key feature is that all model deduced conditional choice probabilities are well formulated in closed form expressions. For reasons people may want to have discrete choice models with disturbance other than extreme type I distributed one to overcome some of its implications, e.g the property of independence with irrelevant alternatives among others. The challenge to meet such requirement is that the key feature of closed form expressions does not hold anymore, and techniques like simulation should be used. My PhD thesis provides the foundation and framework on how to practice the simulation based estimation for discrete choice models with rather flexible fixed effects. This framework is both theoretical and practical, I show how to construct the simulation based estimation and study conditions about both the property of model and practice of simulation under which the estimator is consistent. This object is achieved in two steps. I first develop the theory for static discrete choice models where outcomes of behavior does not depend on previous outcomes. In this case specification of disturbance could be rather free and even serial correlation could be included. Later on, I extend the framework to dynamic discrete choice models, where current behavior depends on some state variables which depend on previous behavior in turn. In dynamic models, specification for disturbance is still free except that serial correlation could not be allowed. These two steps consist of the first and second chapters, in both chapter a numeric example is given which shows how well the simulation based estimator works. In the last chapter I turn to the real data and apply my method to the problem of career decision of young men. Essentially this is a typical application of dynamic programming discrete choice model, which means individual’s object function is the lifetime utility and it depends on both previous behavior and future states and what individual should decide is not only the current behavior but also future actions. By introducing a reduced form of the future utility I succeed in fitting this problem into the framework of dynamic discrete choice model with fixed effects.