KPSS tests on nonlinear transformations of fractional integration time series: A Monte Carlo studytime series: A Monte Carlo study

碩士 === 國立臺北大學 === 經濟學系 === 95 === When economists use Dickey-Fuller type unit root tests to detect nonlinear transformations of I(d) series, the test powers of Dickey-Fuller type unit root test are low in general. The main reason to generate low power problem is the characteristic of I(d) series cha...

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Main Authors: Tsai, Yi-Ting, 蔡佾廷
Other Authors: Wang, Chien-Ho
Format: Others
Language:zh-TW
Published: 2006
Online Access:http://ndltd.ncl.edu.tw/handle/55953021602805746624
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spelling ndltd-TW-095NTPU03890102015-10-13T18:16:00Z http://ndltd.ncl.edu.tw/handle/55953021602805746624 KPSS tests on nonlinear transformations of fractional integration time series: A Monte Carlo studytime series: A Monte Carlo study KPSS檢定用於非線性轉換之分數性整合數列之研究 Tsai, Yi-Ting 蔡佾廷 碩士 國立臺北大學 經濟學系 95 When economists use Dickey-Fuller type unit root tests to detect nonlinear transformations of I(d) series, the test powers of Dickey-Fuller type unit root test are low in general. The main reason to generate low power problem is the characteristic of I(d) series changed after I(d) series have been transformed by some functional form. In this paper, we use KPSS type stationary test to investigate the characteristics of transformed I(d) process. We compare the powers of KPSS statistic and Dickey- Fuller unit root statistic, and try to find the reasons that two statistics have different powers. From Monte Carlo simulation, when , KPSS tests have higher power than traditional Dickey-Fuller type unit root tests. But , KPSS and traditional Dickey-Fuller type unit root tests all have misjudgement for the characteristics of transformed I(d) process. KPSS tests will have the same lower power as Dickey-Fuller unit root tests. Wang, Chien-Ho 王健合 2006 學位論文 ; thesis 54 zh-TW
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language zh-TW
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sources NDLTD
description 碩士 === 國立臺北大學 === 經濟學系 === 95 === When economists use Dickey-Fuller type unit root tests to detect nonlinear transformations of I(d) series, the test powers of Dickey-Fuller type unit root test are low in general. The main reason to generate low power problem is the characteristic of I(d) series changed after I(d) series have been transformed by some functional form. In this paper, we use KPSS type stationary test to investigate the characteristics of transformed I(d) process. We compare the powers of KPSS statistic and Dickey- Fuller unit root statistic, and try to find the reasons that two statistics have different powers. From Monte Carlo simulation, when , KPSS tests have higher power than traditional Dickey-Fuller type unit root tests. But , KPSS and traditional Dickey-Fuller type unit root tests all have misjudgement for the characteristics of transformed I(d) process. KPSS tests will have the same lower power as Dickey-Fuller unit root tests.
author2 Wang, Chien-Ho
author_facet Wang, Chien-Ho
Tsai, Yi-Ting
蔡佾廷
author Tsai, Yi-Ting
蔡佾廷
spellingShingle Tsai, Yi-Ting
蔡佾廷
KPSS tests on nonlinear transformations of fractional integration time series: A Monte Carlo studytime series: A Monte Carlo study
author_sort Tsai, Yi-Ting
title KPSS tests on nonlinear transformations of fractional integration time series: A Monte Carlo studytime series: A Monte Carlo study
title_short KPSS tests on nonlinear transformations of fractional integration time series: A Monte Carlo studytime series: A Monte Carlo study
title_full KPSS tests on nonlinear transformations of fractional integration time series: A Monte Carlo studytime series: A Monte Carlo study
title_fullStr KPSS tests on nonlinear transformations of fractional integration time series: A Monte Carlo studytime series: A Monte Carlo study
title_full_unstemmed KPSS tests on nonlinear transformations of fractional integration time series: A Monte Carlo studytime series: A Monte Carlo study
title_sort kpss tests on nonlinear transformations of fractional integration time series: a monte carlo studytime series: a monte carlo study
publishDate 2006
url http://ndltd.ncl.edu.tw/handle/55953021602805746624
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