Abstract

http://ssrn.com/abstract=875065
 
 

References (46)



 
 

Citations (6)



 


 



Bayesian Model Uncertainty in Smooth Transition Autoregressions


Hedibert F. Lopes


University of Chicago - Booth School of Business

Esther Salazar


Duke University; Universidade Federal do Rio de Janeiro (UFRJ)


Journal of Time Series Analysis, Vol. 27, No. 1, pp. 99-117, January 2006

Abstract:     
In this paper, we propose a fully Bayesian approach to the special class of nonlinear time-series models called the logistic smooth transition autoregressive (LSTAR) model. Initially, a Gibbs sampler is proposed for the LSTAR where the lag length, k, is kept fixed. Then, uncertainty about k is taken into account and a novel reversible jump Markov Chain Monte Carlo (RJMCMC) algorithm is proposed. We compared our RJMCMC algorithm with well-known information criteria, such as the Akaike information criteria, the Bayesian information criteria (BIC) and the deviance information criteria. Our methodology is extensively studied against simulated and real-time series.

Number of Pages in PDF File: 19

Keywords: Markov Chain Monte Carlo, nonlinear time-series model, model selection, reversible jump MCMC; deviance information criterion

Accepted Paper Series


Date posted: February 21, 2006  

Suggested Citation

Lopes, Hedibert F. and Salazar, Esther, Bayesian Model Uncertainty in Smooth Transition Autoregressions. Journal of Time Series Analysis, Vol. 27, No. 1, pp. 99-117, January 2006. Available at SSRN: http://ssrn.com/abstract=875065 or http://dx.doi.org/10.1111/j.1467-9892.2005.00455.x

Contact Information

Hedibert F. Lopes (Contact Author)
University of Chicago - Booth School of Business ( email )
5807 S. Woodlawn Avenue
Chicago, IL 60637
United States
Esther Salazar
Duke University ( email )
Durham, NC 27708
United States
HOME PAGE: http://www.duke.edu/~es145
Universidade Federal do Rio de Janeiro (UFRJ) ( email )
Rua General Canabarro, 706
terreo - Bairro Maracana
20271-201 - Rio de Janeiro, RJ, 23890000
Brazil
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