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Bayesian Analysis of Nonlinear Time Series Models with Threshold

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Author Info
Lubrano, M.

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Abstract

This paper considers the Bayesian analysis of threshold regression models. It shows that this analysis can be conducted with simple deterministic numerical integration in one or two dimensions. The shape of the posterior density is greatly determined by the type of threshold and of transition considered.

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Publisher Info
Paper provided by Universite Aix-Marseille III in its series G.R.E.Q.A.M. with number 96a12.

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Length: 33 pages
Date of creation: 1996
Date of revision:
Handle: RePEc:fth:aixmeq:96a12

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Related research
Keywords: ECONOMETRICS TIME SERIES STATISTICS

Other versions of this item:

Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models
C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other

Cited by:
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  1. Michael J. Dueker & Martin Sola & Fabio Spagnolo, 2006. "Contemporaneous threshold autoregressive models: estimation, testing and forecasting," Working Papers 2003-024, Federal Reserve Bank of St. Louis. [Downloadable!]
    Other versions:
  2. Simon M. Potter, 1999. "Nonlinear time series modelling: an introduction," Staff Reports 87, Federal Reserve Bank of New York. [Downloadable!]
    Other versions:
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