It is often desirable to compare risky investments in the context of economic
decision theory. Expected utility analyses are means by which stochastic alternatives
can be ranked by re-weighting the probability mass using a decision-making agentâÂÂs
utility function. By maximizing expected utility, an agent seeks to balance expected
returns with the inherent risk in each investment alternative. This can be accomplished
by ranking prospects based on the certainty equivalent associated with each
alternative.
In instances where only a small sample of observed data is available to estimate
the underlying distributions of the risky options, reliable inferences are difficult
to make. In this process of comparing alternatives, when estimating explicit probability
forms or nonparametric densities, the variance of the estimate, in this case
the certainty equivalent, is often ignored. Resampling methods allow for estimating
dispersion for a statistic when no parametric assumptions are made about the underlying
distribution. An objective of this dissertation is to utilize these methods to
estimate confidence regions for the sample certainty equivalents of the alternatives
over a subset of the parameter space of the utility function. A second goal of this research is to formalize a testing procedure when dealing
with preference ranking with respect to utility. This is largely based on MeyerâÂÂs
work (1977b) developing stochastic dominance with respect to a function and more
specific testing procedures outlined by Eubank et. al. (1993). Within this objective,
the asymptotic distribution of the test statistic associated with the hypothesis of
preference of one risky outcome over another given a sub-set of the utility function
parameter space is explored.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/4171 |
| Date | 30 October 2006 |
| Creators | Schumann, Keith Daniel |
| Contributors | Eubank, Randall L., Richardson, James W. |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 833103 bytes, electronic, application/pdf, born digital |
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