@mastersthesis {6244,
title = {Multivariate fractional response models in a panel setting with an application to portfolio allocation},
volume = {3558362},
year = {2013},
note = {Copyright - Copyright ProQuest, UMI Dissertations Publishing 2013 Last updated - 2013-05-16 First page - n/a},
month = {2013},
pages = {131},
school = {Michigan State University},
type = {Ph.D.},
abstract = {Several papers use subjective survival probabilities as a measure of mortality risk in studying economic behavior. The first chapter "Wealth Holdings, Asset Allocation and Mortality: A Test of the Information Content of Subjective Survival Probabilities" studies whether subjective survival probability measures contain any additional information that can explain differential wealth holdings and asset allocation among households. We find some evidence that survival probabilities can explain differences in household wealth holding and allocation once we control for other factors that affect decision-making. We also find that the estimated impact of subjective survival is sensitive to the inclusion of reported survival probabilities of one. Some fractional response variables, like the proportion of financial wealth allocated across multiple assets, must satisfy an adding up restriction. In the second chapter "A Model for Multivariate Fractional Responses with an Application to Asset Allocation", we develop a twostep procedure where we estimate a model with multiple fractional response variables exploiting the fact that these variables sum to one in each period and are correlated over time. The first step entails estimation of the multivariate fractional responses using the multinomial quasi-likelihood function which explicitly imposes the adding-up restriction and the second step uses the Classical Minimum Distance estimator to account for serial correlation. Many panel data estimators implicitly assume that we have a balanced panel at our disposal. Unfortunately this is rarely the case and dropping observations is an unsatisfactory solution to the problem. Estimation of fractional responses in a panel requires assumptions about the distribution of the unobserved effect and its relationship with observables, which requires special treatment in an unbalanced panel. In the third chapter, "Estimation of a Multivariate Fractional Response Model with Unbalanced Panel Data", we extend the approach in Wooldridge (2010) to the case of multiple fractional responses and apply this to unbalanced panel data on the allocation of financial wealth across several assets.},
keywords = {Methodology, Net Worth and Assets},
author = {Carlton, Michael Anthony}
}