• 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.

• Communications for Statistical Applications and Methods
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• v.23 no.6
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• pp.587-592
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• 2016

It is a long-standing issue to correctly determine the number of long-run relationships among time series processes. We revisit nonparametric test for cointegration rank and propose bootstrap refinements. Consistent with model-free nature of the tests, we make use of Cholesky factor bootstrap methods, which require weak conditions for data generating processes. Simulation studies show that the original Breitung's test have difficulty in obtaining the correct size due to dependence in cointegrated errors. Our proposed bootstrapped tests considerably mitigate size distortions and represent a complementary approach to other bootstrap refinements, including sieve methods.

• 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.

• 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.

• 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.

• Journal of Korea Water Resources Association
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• v.54 no.2
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• pp.135-144
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• 2021

This paper attempted to find implications for water resource management and water quality improvement by analyzing the causal relationship among discharge, water temperature and pollution index, which were expected to have a great effect on water quality with the rise of water temperature and precipitation change as the warming effect in recent years. For this purpose, the unit root test, cointegration test, and Granger causal test were carried out for 10 multi-purpose dams in Korean major water systems using time series data on discharge, water temperature, BOD, COD and DO. It was analyzed that the fluctuation of water temperature affected the pollution index more than the fluctuation of discharge volume. Also, Hapcheon dam and Chungju dam were the best water quality management dams based on the high causal relationship between water quality and discharge. The second rank was Daecheong dam. The third-ranking group were Yongdam and Andong dam, whose causal relationships between water quality and discharge were low. The last group were the remaining five dams.