Essays on Neural Network Sampling Methods and Instrumental Variables
Lennart Hoogerheide defends his thesis Essays on Neural Network Sampling Methods and Instrumental Variables on Thursday 29, 2006 at Erasmus University Rotterdam. In his thesis Hoogerheide introduces and explores a class of sampling methods, which can be used in Bayesian analysis to get insight into the posterior density of model parameters. These sampling methods, which make use of neural network approximations to posterior densities, can quickly simulate draws from posterior distributions in many models.
In the second part of the thesis new results are given for instrumental variables (IV) regression models. Particular attention is paid to a well-known IV model of Angrist and Krueger (1991, Quarterly Journal of Economics), who use quarter of birth to form instrumental variables in order to estimate the monetary returns to education. Measuring the effect of education on income is relevant for many decision processes; for example, for government agencies responsible for compulsory schooling laws. It should be noted that there is a connection between the two parts of this thesis: the exposed neural network sampling methods can be especially useful if one desires to get insight into irregularly shaped posterior distributions, and such posteriors may occur in IV regression models.
Note to the editor:
Doctorate Lennert Hoogerheide, Thursday 29, 2006
Location: Woudestein, senaatszaal
Info: Media Relations, (31) 10 408 1216