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Bayesian Factorized Cointegration Analysis
Department of Statistical Science, Duke University, Durham, USA
School of Science and Information, Qingdao Agricultural University, Qingdao, China
- 1 Department of Statistical Science, Duke University, Durham, USA
- 2 School of Science and Information, Qingdao Agricultural University, Qingdao, China
Open Journal of Statistics·Volume 02 (2012)·Pages 504–511·Published 13 December 2012·DOI10.4236/ojs.2012.25065
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Abstract
The concept of cointegration is widely used in applied non-stationary time series analysis to describe the co-movement of data measured over time. In this paper, we proposed a Bayesian model for cointegration test and analysis, based on the dynamic latent factor framework. Efficient computational algorithms are also developed based on Markov Chain Monte Carlo (MCMC). Performance and efficiency of the the model and approaches are assessed by simulated and real data analysis.
KeywordsCointegrationBayesianDynamic FactorNon-StationaryRoot StructureMCMC
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