Bayesian model averaging and identification of structural breaks in time series
AbstractBayesian Model Averaging (BMA) is used for testing for multiple break points in univariate series using conjugate normal-gamma priors. This approach can test for the number of structural breaks and produce posterior probabilities for a break at each point in time. Results are averaged over specifications including: stationary; stationary around trend and unit root models, each containing different types and number of breaks and different lag lengths. The procedures are used to test for structural breaks on 14 annual macroeconomic series and 11 natural resource price series. The results indicate that there are structural breaks in all of the natural resource series and most of the macroeconomic series. Many of the series had multiple breaks. Our findings regarding the existence of unit roots, having allowed for structural breaks in the data, are largely consistent with previous work.
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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Applied Economics.
Volume (Year): 43 (2011)
Issue (Month): 26 ()
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Web page: http://www.tandfonline.com/RAEC20
Other versions of this item:
- Fraser, Iain & Balcombe, Kelvin & Sharma, Abhijit, 2007. "Bayesian Model Averaging and Identification of Structural Breaks in Time Series," MPRA Paper 8676, University Library of Munich, Germany.
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
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- Bai, Jushan, 1999. "Likelihood ratio tests for multiple structural changes," Journal of Econometrics, Elsevier, vol. 91(2), pages 299-323, August.
- Bauwens, Luc & Lubrano, Michel & Richard, Jean-Francois, 2000. "Bayesian Inference in Dynamic Econometric Models," OUP Catalogue, Oxford University Press, number 9780198773139.
- Ahrens, W. Ashley & Sharma, Vijaya R., 1997. "Trends in Natural Resource Commodity Prices: Deterministic or Stochastic?," Journal of Environmental Economics and Management, Elsevier, vol. 33(1), pages 59-74, May.
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