Smooth Test and Its Applications in Economics and Finance

One of the drawbacks of the original smooth test is that it was designed for a one-sample problem with fully specified null distribution, which is not always possible to have in practice. I propose both parametric (for density forecast evaluation) and non-parametric (for comparing two unknown densities) techniques in formulating tests based on the probability integral transforms. In case of parametric applications of density forecast evaluation we have to account for the effect of parameter estimation and dependent data in the implementation of the smooth test. In the non-parametric case of comparing two densities I used the orders of the relative sizes of the two samples to get a consistent test. Monte Carlo simulation of these tests shows good power of size characteristics. I applied the proposed smooth tests to evaluate S&P 500 density forecasts and compare age distribution of insured population in New York.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Economics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Ghosh, Aurobindo

Contributors dc:contributor

• Bera, Anil K.

• Statistics

Language dc:language

eng

Identifier

(MiAaPQ)AAI3086066

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/85533