Hasdemir, Esra

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H.,Esra
Hasdemir,E.
Hasdemir, Esra
Esra, Hasdemir
H., Esra
E.,Hasdemir
E., Hasdemir
Job Title
Doktor Öğretim Üyesi
Email Address
esra.hasdemir@atilim.edu.tr
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International Trade and Logistics
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57300824900
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3

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2

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3

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Author: Hasdemir, Esra

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Now showing 1 - 2 of 2
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    Article
    Citation - WoS: 0
    Citation - Scopus: 0
    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; Economics; International Trade and Logistics
    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.
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    Article
    Citation - WoS: 2
    Citation - Scopus: 3
    High Persistence and Nonlinear Behavior in Financial Variables: a More Powerful Unit Root Testing in the Estar Framework
    (Mdpi, 2021) Omay, Tolga; Corakci, Aysegul; Hasdemir, Esra; Economics; International Trade and Logistics
    In this study, we consider the hybrid nonlinear features of the Exponential Smooth Transition Autoregressive-Fractional Fourier Function (ESTAR-FFF) form unit root test. As is well known, when developing a unit root test for the ESTAR model, linearization is performed by the Taylor approximation, and thereby the nuisance parameter problem is eliminated. Although this linearization process leads to a certain amount of information loss in the unit root testing equation, it also causes the resulting test to be more accessible and consistent. The method that we propose here contributes to the literature in three important ways. First, it reduces the information loss that arises due to the Taylor expansion. Second, the research to date has tended to misinterpret the Fourier function used with the Kapetanios, Shin and Snell (2003) (KSS) unit root test and considers it to capture multiple smooth transition structural breaks. The simulation studies that we carry out in this study clearly show that the Fourier function only restores the Taylor residuals of the ESTAR type function rather than accounting forthe smooth structural break. Third, the new nonlinear unit root test developed in this paper has very strong power in the highly persistent near unit root environment that the financial data exhibit. The application of the Kapetanios Shin Snell- Fractional Fourier (KSS-FF) test to ex-post real interest rates data of 11 OECD countries for country-specific sample periods shows that the new test catches nonlinear stationarity in many more countries than the KSS test itself.