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Tests for cointegration with structural breaks based on subsamples

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Listed author(s):
  • Davidson, James
  • Monticini, Andrea

Abstract

Tests for cointegration with allowance for structural breaks using the extrema of residual-based tests over subsamples of the data are considered. One motivation for the approach is to formalize the practice of data snooping by practitioners, who may examine subsamples after failing to find a predicted cointegrating relationship. Valid critical values for such multiple testing situations may be useful. The methods also have the advantage of not imposing a form for the alternative hypothesis-in particular slope vs. intercept shifts and single versus multiple breaks-and being comparatively easy to compute. A range of alternative subsampling procedures, including sample splits, incremental and rolling samples are tabulated and compared experimentally. Shiller's annual stock prices and dividends series provide an illustration.

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File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(10)00043-5
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Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 54 (2010)
Issue (Month): 11 (November)
Pages: 2498-2511

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Handle: RePEc:eee:csdana:v:54:y:2010:i:11:p:2498-2511
Contact details of provider: Web page: http://www.elsevier.com/locate/csda

Keywords: Level shift Regime shift Cointegration Brownian motion;

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  • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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Cited by:

  1. De Wachter, Stefan & Tzavalis, Elias, 2012. "Detection of structural breaks in linear dynamic panel data models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3020-3034.
  2. Atle Oglend, Morten E. Lindbäck, and Petter Osmundsen, 2015. "Shale Gas Boom Affecting the Relationship Between LPG and Oil Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
  3. Lindback, Morten & Osmundsen, Petter & Øglend, Atle, 2013. "Shale Gas and the Relationship between U.S. Natural Gas, Liquified Petroleum Gases and Oil Market," UiS Working Papers in Economics and Finance 2013/5, University of Stavanger.
  4. Panopoulou, Ekaterini & Pantelidis, Theologos, 2016. "The Fisher effect in the presence of time-varying coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 495-511.
  5. Martins, Luis F. & Rodrigues, Paulo M.M., 2014. "Testing for persistence change in fractionally integrated models: An application to world inflation rates," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 502-522.

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