Bayesian Methods for Hidden Markov Models: Recursive Computing in the 21st Century
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Bibliographic InfoArticle provided by American Statistical Association in its journal Journal of the American Statistical Association.
Volume (Year): 97 (2002)
Issue (Month): (March)
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- Zhongfang He & John M. Maheu, 2009.
"Real Time Detection of Structural Breaks in GARCH Models,"
Working Paper Series
11_09, The Rimini Centre for Economic Analysis, revised Jan 2009.
- He, Zhongfang & Maheu, John M., 2010. "Real time detection of structural breaks in GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2628-2640, November.
- Zhongfang He & John M Maheu, 2008. "Real Time Detection of Structural Breaks in GARCH Models," Working Papers tecipa-336, University of Toronto, Department of Economics.
- Zhongfang He & John M. Maheu, 2009. "Real Time Detection of Structural Breaks in GARCH Models," Working Papers 09-31, Bank of Canada.
- Penelope A. Smith & Peter M. Summers, 2004. "Identification and normalization in Markov switching models of "business cycles"," Research Working Paper RWP 04-09, Federal Reserve Bank of Kansas City.
- He, Zhongfang, 2009. "Forecasting output growth by the yield curve: the role of structural breaks," MPRA Paper 28208, University Library of Munich, Germany.
- Netzer, Oded & Lattin, James M. & Srinivasan, V. "Seenu", 2007. "A Hidden Markov Model of Customer Relationship Dynamics," Research Papers 1904r, Stanford University, Graduate School of Business.
- Peter Ebbes & Rajdeep Grewal & Wayne DeSarbo, 2010. "Modeling strategic group dynamics: A hidden Markov approach," Quantitative Marketing and Economics, Springer, vol. 8(2), pages 241-274, June.
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