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Recurrence analysis techniques for non-stationary and non-linear data

  • Philip Kostov

    (Queen's University Belfast)

  • John Lingard

    (University of Newcastle)

When analysing food consumption data a number of problems arise when one departs from the comparative statics of conventional demand theory. Two of these properties, non-linearity and non-stationarity present a major challenge for econometric modelling. A new method for time series analysis, namely recurrence analysis, is outlined which allows for robust analysis of data that can not be satisfactorily handled with established econometric methods. The method is explained and applied to specific food consumption data. General implications for empirical modelling of similar data are inferred.

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File URL: http://econwpa.repec.org/eps/mic/papers/0409/0409003.pdf
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Paper provided by EconWPA in its series Microeconomics with number 0409003.

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Length: 22 pages
Date of creation: 15 Sep 2004
Date of revision:
Handle: RePEc:wpa:wuwpmi:0409003
Note: Type of Document - pdf; pages: 22
Contact details of provider: Web page: http://econwpa.repec.org

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  1. Andrew Harvey & Siem Jan Koopman, 2000. "Signal extraction and the formulation of unobserved components models," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 84-107.
  2. Peter C.B. Phillips, 1985. "Understanding Spurious Regressions in Econometrics," Cowles Foundation Discussion Papers 757, Cowles Foundation for Research in Economics, Yale University.
  3. Hoch, Stephen J & Loewenstein, George F, 1991. " Time-Inconsistent Preferences and Consumer Self-Control," Journal of Consumer Research, University of Chicago Press, vol. 17(4), pages 492-507, March.
  4. Joseph Beaulieu, J. & Miron, Jeffrey A., 1993. "Seasonal unit roots in aggregate U.S. data," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 305-328.
  5. Henrik Hansen & Søren Johansen, 1999. "Some tests for parameter constancy in cointegrated VAR-models," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 306-333.
  6. Thaler, Richard, 1981. "Some empirical evidence on dynamic inconsistency," Economics Letters, Elsevier, vol. 8(3), pages 201-207.
  7. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
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