A Regime Shift Model with Nonparametric Switching Mechanism
报告人： Haiqiang Chen, Xiamen University
时间：2016-03-24 14:00 ~ 15:30
地点：Room 217, Guanghua Building 2
In this paper, we propose a new class of regime shift model with a flexible switching mechanism that relies on a nonparametric probability function of the observed threshold variables. The proposed model extends classical threshold models by allowing contaminated threshold variables or heterogeneous threshold values, thus gaining more power in handling complicated data structures. We solve the identification issue by imposing either a global shape restriction or a boundary condition on the nonparametric probability function. We utilize the natural connection between penalized splines and hierarchical Bayes to conduct estimation. By adopting different priors, our procedure works well for estimating smooth curves as well as discontinuous curves with occasional structural breaks. Bayesian tests for the existence of threshold effects are also conducted based on the posterior samples from Markov chain Monte Carlo (MCMC) methods. Both simulation studies and an empirical application in predicting U.S. stock market returns demonstrate the validity of our methods.
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