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Title:Density Estimation for Robust Financial Econometrics
Author(s):Takada, Teruko
Doctoral Committee Chair(s):Koenker, Roger W.
Department / Program:Economics
Discipline:Economics
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Economics, Theory
Abstract:Chapter 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).
Issue Date:2001
Type:Text
Language:English
Description:186 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2001.
URI:http://hdl.handle.net/2142/85513
Other Identifier(s):(MiAaPQ)AAI3023210
Date Available in IDEALS:2015-09-25
Date Deposited:2001


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