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Can we distinguish between common nonlinear time series models and long memory?

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  • Kuswanto, Heri
  • Sibbertsen, Philipp

Abstract

We show that specific nonlinear time series models such as SETAR, LSTAR, ESTAR and Markov switching which are common in econometric practice can hardly be distinguished from long memory by standard methods such as the GPH estimator for the memory parameter or linearity tests either general or against a specific nonlinear model. We show by Monte Carlo that under certain conditions, the nonlinear data generating process can have misleading either stationary or non-stationary long memory properties.

Suggested Citation

  • Kuswanto, Heri & Sibbertsen, Philipp, 2007. "Can we distinguish between common nonlinear time series models and long memory?," Hannover Economic Papers (HEP) dp-380, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  • Handle: RePEc:han:dpaper:dp-380
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    File URL: http://diskussionspapiere.wiwi.uni-hannover.de/pdf_bib/dp-380.pdf
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    Cited by:

    1. Kuswanto, Heri & Sibbertsen, Philipp, 2008. "A Study on "Spurious Long Memory in Nonlinear Time Series Models"," Hannover Economic Papers (HEP) dp-410, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    2. Ulrike Busch & Dieter Nautz, 2010. "Controllability and Persistence of Money Market Rates along the Yield Curve: Evidence from the Euro Area," German Economic Review, Verein für Socialpolitik, vol. 11, pages 367-380, August.
    3. Kuswanto, Heri, 2009. "A New Simple Test Against Spurious Long Memory Using Temporal Aggregation," Hannover Economic Papers (HEP) dp-425, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.

    More about this item

    Keywords

    Nonlinear models; long-range dependencies;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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