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이분산성에 일치하는 장기균형관계의 검정
Reports NRF is supported by Research Projects( 이분산성에 일치하는 장기균형관계의 검정 | 2007 Year | 서병선(고려대학교) ) data is submitted to the NRF Project Results
Researcher who has been awarded a research grant by Humanities and Social Studies Support Program of NRF has to submit an end product within 6 months(* depend on the form of business)
  • Researchers have entered the information directly to the NRF of Korea research support system
Project Number B00089
Year(selected) 2007 Year
the present condition of Project 종료
State of proposition 재단승인
Completion Date 2009년 05월 22일
Year type 결과보고
Year(final report) 2009년
Research Summary
  • Korean
  • 본 논문에서는 비정상적 시계열이 갖는 장기균형관계를 검정하기 위한 검정방법을 연구하였다. 기존의 우도비 (LR) 장기균형검정에서는 동일 분포를 가정하기 때문에 자료가 갖는 이분산성에서 발생하는 문제를 다루지 못했다. 여기서는 이분산성을 고려하여 이분산성에 일치하는 분산추정량에 기초한 라그랑지 승수 (LM) 검정통계량과 Wald 통계량을 제시하였다. 모의 시뮬레이션의 결과 이분산성이 존재할 때 기존의 검정 방식에 비하여 우수한 성과를 보임을 밝혔다.
  • English
  • This paper considers statistical inference on the cointegration rank in vector error correction models, which
    is robust to heteroscedastic errors. As the likelihood ratio (LR) statistic assumes identically distributed
    errors, we develop the Lagrange multiplier (LM) and Wald statistics for the cointegration rank using
    the heteroscedasticity robust covariance estimator. The asymptotic distributions of the LM and Wald
    statistics follow the nonstandard distribution, which has been found by Johansen (1991).
    Simulation evidence indicates that the proposed tests improve
    the performance of the cointegration rank test.
Research result report
  • Abstract
  • This paper considers statistical inference on the cointegration rank in vector error correction models, which
    is robust to heteroscedastic errors. As the likelihood ratio (LR) statistic assumes identically distributed
    errors, we develop the Lagrange multiplier (LM) and Wald statistics for the cointegration rank using
    the heteroscedasticity robust covariance estimator. The asymptotic distributions of the LM and Wald
    statistics follow the nonstandard distribution, which has been found by Johansen (1991).
    Simulation evidence indicates that the proposed tests improve
    the performance of the cointegration rank test.
  • Research result and Utilization method
  • Economic variables show time-varying variance and clustered volatility, and the empirical studies
    have found that the volatility can be explained by the near-IGARCH process. As the LR statistic for
    cointegration rank assumes identically distributed errors, the statistical inference on cointegration rank
    can be affected by conditionally heteroscedastic errors. This study proposes the LM statistic for cointegration rank,
    which involves the heteroscedasticity consistent covariance estimator. The asymptotic distribution of the LM statistic
    follows the nonstandard distribution in the same way as the LR statistic. The size performance of the LM statistic
    exhibit heteroscedasticity robustness even when the errors are near-IGARCH.
  • Index terms
  • Cointegration Rank; ECM; Heteroscedasticity; Near-IGARCH Volatility
  • List of digital content of this reports
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