Feng-Chi Liu

Feng Chia University - Department of Statistics

100 Wen Hwa Road

Taichung, 407

Taiwan

SCHOLARLY PAPERS

3

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177

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1

CROSSREF CITATIONS

14

Scholarly Papers (3)

1.

Bayesian Estimation for Parsimonious Threshold Autoregressive Models in R

The Newsletter of the R Project
Number of pages: 8 Posted: 27 May 2009 Last Revised: 21 Aug 2009
Feng Chia University - Department of Statistics, Graduate Institute of Applied Statistics, Feng Chia University, Feng Chia University - Department of Statistics and University of Sydney
Downloads 96 (274,195)
Citation 1

Abstract:

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Asymmetry; MCMC method; two-regime SETAR model; BAYSTAR package

2.

Heavy-Tailed-Distributed Threshold Stochastic Volatility Models in Financial Time Series

Australian & New Zealand Journal of Statistics, Vol. 50, pp. 1-23, 2008
Number of pages: 30 Posted: 27 May 2009 Last Revised: 27 Aug 2009
Cathy W. S. Chen, Feng-Chi Liu and Mike K. P. So
Feng Chia University - Department of Statistics, Feng Chia University - Department of Statistics and Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics & Operations Management
Downloads 81 (304,971)

Abstract:

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Kalman filter, Markov chain Monte Carlo method, state space model, stochastic volatility models, threshold, value-at-risk

3.

Best Subset Selection of Autoregressive Models with Exogenous Variables and Generalized Autoregressive Conditional Heteroscedasticity Errors

Journal of the Royal Statistical Society, Series C: Applied Statistics, Vol. 55, pp. 201-224
Posted: 27 May 2009 Last Revised: 30 Mar 2010
Mike K. P. So, Cathy W. S. Chen and Feng-Chi Liu
Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics & Operations Management, Feng Chia University - Department of Statistics and Feng Chia University - Department of Statistics

Abstract:

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Autoregressive models with exogenous variables, Bayesian methods, Generalized autoregressive conditional heteroscedasticity models, Markov chain Monte Carlo methods, Stochastic search variable selection, Stock-markets