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An omnibus test to detect time-heterogeneity in time series

This paper focuses on a procedure to test for structural changes in the first two moments of a time series, when no information about the process driving the breaks is available. We model the series as a finite-order auto-regressive process plus an orthogonal Bernstein polynomial to capture heterogeneity. Testing for the null of time-invariance is then achieved by testing the order of the polynomial, using either an information criterion, or a restriction test. The procedure is an omnibus test in the sense that it covers both the pure discrete structural changes and some continuous changes models. To some extent, our paper can be seen as an extension of Heracleous et al. (Econom Rev 27:363–384, 2008).

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File URL: ftp://mse.univ-paris1.fr/pub/mse/CES2010/10098.pdf
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Paper provided by Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne in its series Documents de travail du Centre d'Economie de la Sorbonne with number 10098.

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Length: 24 pages
Date of creation: Dec 2010
Date of revision:
Handle: RePEc:mse:cesdoc:10098
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  1. Lanouar Charfeddine & Dominique Guegan, 2007. "Which is the best model for the US inflation rate : a structural changes model or a long memory process ?," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00188309, HAL.
  2. Perron, P, 1988. "The Great Crash, The Oil Price Shock And The Unit Root Hypothesis," Papers 338, Princeton, Department of Economics - Econometric Research Program.
  3. Catalin Starica & Clive Granger, 2004. "Non-stationarities in stock returns," Econometrics 0411016, EconWPA.
  4. Bruce E. Hansen, 1998. "Testing for Structural Change in Conditional Models," Boston College Working Papers in Economics 310., Boston College Department of Economics.
  5. Bai, Jushan, 1999. "Likelihood ratio tests for multiple structural changes," Journal of Econometrics, Elsevier, vol. 91(2), pages 299-323, August.
  6. BAI, Jushan & PERRON, Pierre, 1998. "Computation and Analysis of Multiple Structural-Change Models," Cahiers de recherche 9807, Universite de Montreal, Departement de sciences economiques.
  7. Altissimo, Filippo & Corradi, Valentina, 2003. "Strong rules for detecting the number of breaks in a time series," Journal of Econometrics, Elsevier, vol. 117(2), pages 207-244, December.
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