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Activity Number: 489
Type: Contributed
Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract - #309137
Title: On Mixture Double Autoregressive Models
Author(s): Zhao Liu*+ and Guodong Li
Companies: The University of Hong Kong and The University of Hong Kong
Keywords: Mixture Double Autoregressive Model ; EM algorithm ; Squared autocorrelation ; Dynamic Mixture Proportion ; Multimodal Predictive Distribution ; Stationarity

This paper proposes a mixture double autoregressive (MDAR) model for nonlinear time series analysis. The model is composed by a mixture of K double autoregressive components, which was first proposed by Ling(2007). In this paper, we also study some extensions of MDAR model, for example the one with a dynamic mixture proportion. These types of model inherits the good property of the double AR model and the mixture model. What's more, the MDAR model also owns a flexible squared autocorrelation structure. Theoretical property of MDAR model is studied such as the stationarity conditions, autocorrelation function, and squared autocorrelation functions. The estimation procedure is addressed using EM algorithm, and BIC criteria handles the model selection problem well. Multiple step predictive distributions are constructed to demonstrate the shape changing characteristics of the multimodal conditional distributions. The heteroscedasticity of time series is also well depicted. We compare the MDAR model with other mainstream models based on the SP Index and the MDAR models appear to capture characteristics of the data more significantly.

Authors who are presenting talks have a * after their name.

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