This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 176
Type: Contributed
Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract - #308831
Title: Statistical Inference for the Probabilistic Spectral Density
Author(s): Junbum Lee*+
Companies: Texas A&M University
Address: 3143 TAMU, College Station, TX, 77843, United States
Keywords: spectral density ; time series
Abstract:

In time series analysis there arises several situations where the traditional spectral density function does not contain much information about the underlying model and may not even be well defined. Such examples, include GARCH-type models. In this talk we define a new spectral density function, which we call the probabilistic spectral density, which is well defined for a large class of processes and contains information about the dynamics which drive the process. We propose nonparametric methods for estimating the probabilistic spectral density and derive their asymptotic sampling properties, in particular showing asymptotic normality. We apply the probabilistic spectral density to the selection of not necessarily nested time series models.


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