JSM 2005 - Toronto

Abstract #304033

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Activity Number: 360
Type: Contributed
Date/Time: Wednesday, August 10, 2005 : 8:30 AM to 10:20 AM
Sponsor: Business and Economics Statistics Section
Abstract - #304033
Title: Statistical Inferences on Time Series with Dependent Innovations
Author(s): Wanli Min*+
Companies: IBM
Address: 1101 Kitchawan Road and Route 134, Yorktown Heights, NY, 10598, United States
Keywords: Invariance Principle ; Central Limit Theroem ; Information Criteria ; Linear Processes
Abstract:

The asymptotic behavior of sample autocovariance and partial sums of a linear process with iid innovations has been studied extensively. We will consider the same problem for general linear processes whose innovations are dependent processes, including Threshold AutoRegressive (TAR) and Generalized AutoRegressive Conditional Heteroscadetic (GARCH) processes. Central limit theorems and invariance principles are established under mild conditions within the framework, which does not require mixing conditions. We further investigate the effect of dependent innovations on model selection based on Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), sample Autocorrelation function (ACF), and partial ACF, which originally are derived with assumption of iid innovations.

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