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An Omnibus Test to Detect Time-Heterogeneity in Time Series

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  • Dominique Guegan

    ()
    (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris I - Panthéon-Sorbonne, EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics - Ecole d'Économie de Paris)

  • Philippe De Peretti

    ()
    (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris I - Panthéon-Sorbonne)

Abstract

In this paper, we present a procedure that tests for the null of time-homogeneity of the first two moments of a time-series. Whereas the literature dedicated to structural breaks testing procedures often focuses on one kind of alternative, i.e. discrete shifts or smooth transition, our procedure is designed to deal with a broader alternative including i) discrete shifts, ii) smooth transition, iii) time-varying moments, iv) probability-driven breaks, v) GARCH or Stochastic Volatility Models for the variance. Our test uses the recently introduced maximum entropy bootstrap, designed to capture both time-dependency and time-heterogeneity. Running simulations, our procedure appears to be quite powerful. To some extent, our paper is an extension of Heracleous, Koutris and Spanos (2008).

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Paper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00721327.

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Date of creation: 27 Jul 2012
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Handle: RePEc:hal:cesptp:halshs-00721327

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Keywords: Time-homogeneity; maximum entropy bootstrap;

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  1. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, Econometric Society, vol. 61(4), pages 821-56, July.
  2. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, Elsevier, vol. 45(1-2), pages 39-70.
  3. Altissimo, Filippo & Corradi, Valentina, 2003. "Strong rules for detecting the number of breaks in a time series," Journal of Econometrics, Elsevier, Elsevier, vol. 117(2), pages 207-244, December.
  4. Andreas Koutris & Maria Heracleous & Aris Spanos, 2008. "Testing for Nonstationarity Using Maximum Entropy Resampling: A Misspecification Testing Perspective," Econometric Reviews, Taylor & Francis Journals, Taylor & Francis Journals, vol. 27(4-6), pages 363-384.
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  9. Perron, Pierre, 1989. "The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis," Econometrica, Econometric Society, Econometric Society, vol. 57(6), pages 1361-1401, November.
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  13. Vinod, H. D., 2004. "Ranking mutual funds using unconventional utility theory and stochastic dominance," Journal of Empirical Finance, Elsevier, Elsevier, vol. 11(3), pages 353-377, June.
  14. Lanouar Charfeddine & Dominique Guégan, 2007. "Which is the best model for the US inflation rate : a structural changes model or a long memory," Documents de travail du Centre d'Economie de la Sorbonne, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne b07061, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  15. Catalin Starica & Clive Granger, 2004. "Non-stationarities in stock returns," Econometrics, EconWPA 0411016, EconWPA.
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