Essays in Likelihood-Based Computational Econometrics

PhD Thesis# 562
Author:
Dr. Tim (T.) Salimans
Supervisor(s):
Prof. R. Paap, Prof. D. Fok
Date:
2013-05-23

Abstract

Econometrics relies on probabilistic models to describe and analyze how observed data relates to economic hypotheses, and to provide a rigorous framework for reasoning under uncertainty. Statistical analysis of such models can be performed using likelihood-based inference methods such as Bayesian analysis and the method of maximum likelihood. This dissertation deals with the computational challenges associated with these methods, using Monte Carlo methods as well as deterministic approximations of the (marginal) likelihood. By developing efficient approximate inference algorithms, this work addresses various problems in economics for which likelihood-based econometric inference was previously difficult or infeasible.

Publisher of the TI-theses is: Rozenberg Publishing Services