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Essays on Likelihood Ratio Test for ...
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Zhang, Li.
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Essays on Likelihood Ratio Test for Markov Switching.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Essays on Likelihood Ratio Test for Markov Switching./
作者:
Zhang, Li.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:
48 p.
附註:
Source: Dissertations Abstracts International, Volume: 85-10, Section: A.
Contained By:
Dissertations Abstracts International85-10A.
電子資源:
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30997218
ISBN:
9798381981155
Essays on Likelihood Ratio Test for Markov Switching.
Zhang, Li.
Essays on Likelihood Ratio Test for Markov Switching.
- Ann Arbor : ProQuest Dissertations & Theses, 2024 - 48 p.
Source: Dissertations Abstracts International, Volume: 85-10, Section: A.
Thesis (Ph.D.)--Washington University in St. Louis, 2024.
I propose a likelihood ratio test for fixed unit root against time switching unit root models. Our test is different from the existing jump detection literature centered on the application of various forms of Augmented Dickey-Fuller tests. Our methodology involves the inclusion of random coefficients, which effectively capture both expansion and contraction behaviors. We show that the contiguous alternatives converge to the null hypothesis at the order of T-3/4, where T is the sample size. Our test is asymptotically optimal in the sense that it maximizes a weighted power function. We derive the asymptotic distribution of our test under the null and local alternatives.
ISBN: 9798381981155Subjects--Index Terms:
Dickey-Fuller tests
Essays on Likelihood Ratio Test for Markov Switching.
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I propose a likelihood ratio test for fixed unit root against time switching unit root models. Our test is different from the existing jump detection literature centered on the application of various forms of Augmented Dickey-Fuller tests. Our methodology involves the inclusion of random coefficients, which effectively capture both expansion and contraction behaviors. We show that the contiguous alternatives converge to the null hypothesis at the order of T-3/4, where T is the sample size. Our test is asymptotically optimal in the sense that it maximizes a weighted power function. We derive the asymptotic distribution of our test under the null and local alternatives.
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