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Seasonal Unit Root Tests for Trending and Breaking Series with Application to Industrial Production

Author

Listed:
  • Westerlund, Joakim

    (Department of Economics, School of Business, Economics and Law, Göteborg University)

  • Costantini, Mauro

    (University of Vienna)

  • Narayan, Paresh

    (Deakin University)

  • Popp, Stephan

    (University of Duisburg–Essen)

Abstract

Some unit root testing situations are more difficult than others. In the case of quarterly industrial production there is not only the seasonal variation that needs to be considered but also the occasionally breaking linear trend. In the current paper we take this as our starting point to develop three new seasonal unit root tests that allow for a break in both the seasonal mean and linear trend of a quarterly time series. The asymptotic properties of the tests are derived and investigated in small-samples using simulations. In the empirical part of the paper we consider as an example the industrial production of 13 European countries. The results suggest that for most of the series there is evidence of stationary seasonality around an otherwise nonseasonal unit root.

Suggested Citation

  • Westerlund, Joakim & Costantini, Mauro & Narayan, Paresh & Popp, Stephan, 2009. "Seasonal Unit Root Tests for Trending and Breaking Series with Application to Industrial Production," Working Papers in Economics 377, University of Gothenburg, Department of Economics.
    • Handle: RePEc:hhs:gunwpe:0377
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    File URL: http://hdl.handle.net/2077/21047
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    References listed on IDEAS

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    More about this item

    Keywords

    Seasonal unit root tests; Structural breaks; Linear time trend; Industrial production;
    All these keywords.

    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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