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Activity Number: 71
Type: Contributed
Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
Sponsor: IMS
Abstract #316429 View Presentation
Title: Bayesian Analysis for Nonparametric Regime Shift Models
Author(s): Yingxing Li*
Companies: Xiamen University
Keywords: regime shifts ; nonparametric ; threshold effects ; splines ; MCMC ; Bayesian inference
Abstract:

In this paper, we propose a new class of regime shift models with threshold variables driven by nonparametric switching mechanism. The switching dynamics relies on an unknown link function of the observable threshold variables, and hence provides more flexibility in revealing the true nature of the underlying process. Moreover, the proposed models generally embrace traditional threshold models with contaminated threshold variables or heterogeneous threshold parameters, thus gaining more power in handling complicated situations. We model the unknown link function via splines and employ Markov Chain Monte Carlo (MCMC) methods for estimation. Bayesian tests for the existence of threshold effects are also constructed. Simulation studies and an empirical application to the stock return predictability both demonstrated the validity of our methods.


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