Hasdemir, Esra

Profile Picture
Name Variants
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
ORCID ID
Scopus Author ID
57300824900
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Scholarly Output

3

Articles

2

Citation Count

3

Supervised Theses

0

Filters

2019 - 201912020 - 20242
Reset filters

Settings

Scholarly Output Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Conference Object
    Citation Count: 1
    Fiscal Sustainability from a Nonlinear Framework: Evidence from 14 European Countries
    (Springer Science and Business Media B.V., 2019) Hasdemir,E.; Omay,T.; Department of Basic English (Prep School); International Trade and Logistics; Economics
    This study examines the fiscal sustainability of 14 European Union (EU) Member countries in the long run. For this purpose, a linear Augmented Dickey Fuller (ADF) and a variety of nonlinear univariate unit root tests are applied to the debt-to-GDP series of the 14 EU Member countries; Belgium, Czech Republic, Denmark, Finland, France, Greece, Hungary, Italy, Netherlands, Poland, Portugal, Romania, Slovakia and Sweden. In addition to that, the nonlinear unit root tests applied in this study are classified according to the source of nonlinearities: (i) time dependent nonlinearity (structural break(s)), (ii) state dependent nonlinearity and (iii) hybrid nonlinearity. Thus, the nonlinearities and their sources in data generating process of debt-to-GDP series of every country can be determined. The findings of this study show that the null of linear unit root cannot be rejected for none of the countries by applying linear ADF whereas it can be rejected as a result of nonlinear unit root tests for considerable number of countries, i.e. 11 out of 14 countries exhibit time dependent nonlinearity, 6 out of 14 exhibit state dependent nonlinearity and 10 out of 14 exhibit hybrid nonlinearity in their relevant data. So, the source of nonlinearities in the relevant data differs according to the country. That is, for testing the fiscal sustainability, the nonlinearities in the data need to be taken into account. Ignoring the nonlinearities in the testing procedure can lead misleading results in the decision of fiscal sustainability in the long run. © 2019, Springer Nature Switzerland AG.
  • Thumbnail Image
    Article
    Citation Count: 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.
  • Thumbnail Image
    Article
    Citation Count: 2
    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.