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

Author

Listed:
  • Philip Kostov

    (Queen's University Belfast)

  • John Lingard

    (University of Newcastle)

Abstract

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.

Suggested Citation

  • Philip Kostov & John Lingard, 2004. "Recurrence analysis techniques for non-stationary and non-linear data," Microeconomics 0409003, EconWPA.
  • Handle: RePEc:wpa:wuwpmi:0409003
    Note: Type of Document - pdf; pages: 22
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/mic/papers/0409/0409003.pdf
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    References listed on IDEAS

    as
    1. Hoch, Stephen J & Loewenstein, George F, 1991. " Time-Inconsistent Preferences and Consumer Self-Control," Journal of Consumer Research, Oxford University Press, vol. 17(4), pages 492-507, March.
    2. 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.
    3. 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.
    4. Thaler, Richard, 1981. "Some empirical evidence on dynamic inconsistency," Economics Letters, Elsevier, vol. 8(3), pages 201-207.
    5. Phillips, P.C.B., 1986. "Understanding spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 33(3), pages 311-340, December.
    6. 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.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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