Hasdemir, Esra

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https://hdl.handle.net/20.500.14411/7018
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H.,Esra
Hasdemir,E.
Hasdemir, Esra
Esra, Hasdemir
H., Esra
E.,Hasdemir
E., Hasdemir
Job Title
Doktor Öğretim Üyesi
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esra.hasdemir@atilim.edu.tr
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International Trade and Logistics
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57300824900
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Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi (Online)1
Computational Economics1
Mathematics1
Springer Proceedings in Business and Economics -- 4th International Conference on Banking and Fice Perspectives, ICBFP 2019 -- 2 May 2019 through 3 May 2019 -- Famagusta -- 2737291
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    Article
    Citation - WoS: 2
    Citation - Scopus: 1
    Comparison of the Performance of Structural Break Tests in Stationary and Nonstationary Series: a New Bootstrap Algorithm
    (Springer, 2024) Camalan, Ozge; Hasdemir, Esra; Omay, Tolga; Kucuker, Mustafa Can
    Structural breaks are considered as permanent changes in the series mainly because of shocks, policy changes, and global crises. Hence, making estimations by ignoring the presence of structural breaks may cause the biased parameter value. In this context, it is vital to identify the presence of the structural breaks and the break dates in the series to prevent misleading results. Accordingly, the first aim of this study is to compare the performance of unit root with structural break tests allowing a single break and multiple structural breaks. For this purpose, firstly, a Monte Carlo simulation study has been conducted through using a generated homoscedastic and stationary series in different sample sizes to evaluate the performances of these tests. As a result of the simulation study, Zivot and Andrews (J Bus Econ Stat 20(1):25-44, 1992) are the best-performing tests in capturing a single break. The most powerful tests for the multiple break setting are those developed by Kapetanios (J Time Ser Anal 26(1):123-133, 2005) and Perron (Palgrave Handb Econom 1:278-352, 2006). A new Bootstrap algorithm has been proposed along with the study's primary aim. This newly proposed Bootstrap algorithm calculates the optimal number of statistically significant structural breaks under more general assumptions. Therefore, it guarantees finding an accurate number of optimal breaks in real-world data. In the empirical part, structural breaks in the real interest rate data of the US and Australia resulting from policy changes have been examined. The results concluded that the bootstrap sequential break test is the best-performing approach due to the general assumption made to cover real-world data.