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University of Illinois at Urbana-Champaign
Density Estimation for Robust Financial Econometrics
Abstract
dc:descriptionChapter 3 introduces an efficient and robust parametric inference which minimizes the Hellinger distance between two nonparametrically smoothed density estimates: the simulated model density and corresponding observed density. This approach generalizes work of Beran (1977) and Basu and Lindsay (1994) so that dependent data and simulated model densities are allowed, enabling the estimation without simple analytical criterion functions. In application to the lognormal stochastic volatility model, the proposed estimator is found to be competitive with the Markov-chain Monte Carlo approach of Jacquier, Polson, and Rossi (1994).
Degree
thesis:*- 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 and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Takada, Teruko
- Contributors dc:contributor
-
- Koenker, Roger W.
Subjects
dc:subject × 1Rights
- Language dc:language
- eng
Identifiers
dc:identifier.*- Identifier
- (MiAaPQ)AAI3023210
- OAI identifier oai:identifier
- oai:www.ideals.illinois.edu:2142/85513