Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.518778
Title: Essays on semiparametric estimation of Markov decision processes
Author: Srisuma, Sorawoot
Awarding Body: London School of Economics and Political Science
Current Institution: London School of Economics and Political Science (University of London)
Date of Award: 2010
Availability of Full Text:
Access through EThOS:
Full text unavailable from EThOS. Please try the link below.
Access through Institution:
Abstract:
Dynamic models of forward looking agents, whose goal is to maximize expected in-tertemporal payoffs, are useful modelling frameworks in economics. With an exception of a small class of dynamic decision processes, the estimation of the primitives in these models is computationally burdensome due to the presence of the value functions that has no closed form. We follow a popular two-step approach which estimates the functions of interest rather than use direct numerical approximation. The first chapter, joint with Oliver Linton, considers a class of dynamic discrete choice models that contain observable continuously distributed state variables. Most papers on the estimation of dynamic discrete choice models assume that the observable state variables can only take finitely many values. We show that the extension to the infinite dimensional case leads to a well-posed inverse problem. We derive the distribution theory for the finite and the infinite dimensional parameters. Dynamic models with continuous choice can sometimes avoid the numerical issues related to the value function through the use of Euler's equation. The second chapter considers models with continuous choice that do not necessarily belong to the Euler class but frequently arise in applied problems. In this chapter, a class of minimum distance estimators is proposed, their distribution theory along with the infinite dimensional parameters of the decision models are derived. The third chapter demonstrates how the methodology developed for the discrete and continuous choice problems can be adapted to estimate a variety of other dynamic models. The final chapter discusses an important problem, and provides an example, where some well-known estimation procedures in the literature may fail to consistently estimate an identified model. The estimation methodologies I propose in the preceding chapters may not suffer from the problems of this kind.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.518778  DOI: Not available
Share: