This thesis consists of two parts. In the first part a class of sampling methods, which can be used in Bayesian analysis to get insight into the posterior density of model parameters, is introduced and explored. 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 this 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 ex! posed 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.

Additional Metadata
Keywords Monte Carlo integration, instrumental variables, neural networks, returns to education, weak instruments
Promotor H.K. van Dijk (Herman)
Publisher Erasmus University Rotterdam , Thela Thesis, Amsterdam
Sponsor Dijk, Prof. Dr. H.K. van (promotor)
Persistent URL hdl.handle.net/1765/7847
Citation
Hoogerheide, L.F. (2006, June 29). Essays on Neural Network Sampling Methods and Instrumental Variables. Thela Thesis, Amsterdam. Retrieved from http://hdl.handle.net/1765/7847