Bayesian Subset Selection for Two-Threshold Variable Autoregressive Models
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Shuxia Ni
und
Jinshan Liu
Abstract
In this paper, we propose and study an effective Bayesian subset selection method for two-threshold variable autoregressive (TTV-AR) models. The usual complexity of model selection is increased by capturing the uncertainty of the two unknown threshold levels and the two unknown delay lags. By using Markov chain Monte Carlo (MCMC) techniques with driven by a stochastic search, we can identify the best subset model from a large number of possible choices. Simulation experiments show that the proposed method works very well. As applied to the application to the Hang Seng index, we successfully distinguish the best subset TTV-AR model.
Acknowledgments
We thank the Editor and the Referee(s) for their insightful comments and suggestions that help us significantly to improve our manuscript. This research was partially supported by the National Science Foundation of Guangdong Province of China (2016A030313414), Ministry of Education in China Project of Humanities and Social Sciences (17YJA910002) and the Major Research Plan of the National Natural Science Foundation of China (91746102).
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Supplementary Material
The online version of this article offers supplementary material (DOI: https://doi.org/10.1515/snde-2017-0062).
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Artikel in diesem Heft
- Research Articles
- A hidden Markov regime-switching smooth transition model
- The Rescaled VAR Model with an Application to Mixed-Frequency Macroeconomic Forecasting
- A New Method for Specifying the Tuning Parameter of ℓ1 Trend Filtering
- Bayesian Subset Selection for Two-Threshold Variable Autoregressive Models
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Artikel in diesem Heft
- Research Articles
- A hidden Markov regime-switching smooth transition model
- The Rescaled VAR Model with an Application to Mixed-Frequency Macroeconomic Forecasting
- A New Method for Specifying the Tuning Parameter of ℓ1 Trend Filtering
- Bayesian Subset Selection for Two-Threshold Variable Autoregressive Models
- Market concentration and market power of the Swedish mortgage Sector – a wavelet panel efficiency analysis
