• Title/Summary/Keyword: seasonal cointegration rank test

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Seasonal Cointegration Rank Tests for Daily Data

  • Song, Dae-Gun;Park, Suk-Kyung;Cho, Sin-Sup
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.3
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    • pp.695-703
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    • 2005
  • This paper extends the maximum likelihood seasonal cointegration procedure developed by Johansen and Schaumburg (1999) for daily time series. The finite sample distribution of the associated rank test for dally data is also presented.

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Joint Test for Seasonal Cointegrating Ranks

  • Seong, Byeong-Chan;Yi, Yoon-Ju
    • Communications for Statistical Applications and Methods
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    • v.15 no.5
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    • pp.719-726
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    • 2008
  • In this paper we consider a joint test for seasonal cointegrating(CI) ranks that enables us to simultaneously model cointegrated structures across seasonal unit roots in seasonal cointegration. A CI rank test for a single seasonal unit root is constructed and extended to a joint test for multiple seasonal unit roots. Their asymptotic distributions and selected critical values for the joint test are obtained. Through a small Monte Carlo simulation study, we evaluate performances of the tests.

Seasonal cointegration for daily data

  • Song, Dae-Gun;Cho, Sin-Sup;Park, Suk-Kyung
    • Proceedings of the Korean Statistical Society Conference
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    • 2005.05a
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    • pp.13-15
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    • 2005
  • In this paper, we propose an extension of the maximum likelihood seasonal cointegration procedure developed by Johansen and Schaumburg (1999) for daily time series. We presented the finite sample distribution of the associated rank test statistics for daily data.

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INFERENCE ON THE SEASONALLY COINTEGRATED MODEL WITH STRUCTURAL CHANGES

  • Song, Dae-Gun;Cho, Sin-Sup
    • Journal of the Korean Statistical Society
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    • v.36 no.4
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    • pp.501-522
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    • 2007
  • We propose an estimation procedure that can be used for detecting structural changes in the seasonal cointegrated vector autoregressive model. The asymptotic properties of the estimates and the test statistics for the parameter change are provided. A simulation example is presented to illustrate this method and its concept.