Omay, Tolga

Profile Picture
Profile URL
https://hdl.handle.net/20.500.14411/7237
Name Variants
T.,Omay
Omay, Tolga
O., Tolga
Tolga, Omay
Omay,T.
O.,Tolga
T., Omay
Omay T.
Job Title
Profesor Doktor
Email Address
tolga.omay@atilim.edu.tr
Main Affiliation
Economics
Status
Website
ORCID ID
Scopus Author ID
23978235900
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
GDI-4259-2022

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
2
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QUALITY EDUCATION4
QUALITY EDUCATION
0
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GENDER EQUALITY5
GENDER EQUALITY
0
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CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
Research Products
AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
3
Research Products
DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
11
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
3
Research Products
REDUCED INEQUALITIES10
REDUCED INEQUALITIES
2
Research Products
SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
1
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
1
Research Products
CLIMATE ACTION13
CLIMATE ACTION
7
Research Products
LIFE BELOW WATER14
LIFE BELOW WATER
2
Research Products
LIFE ON LAND15
LIFE ON LAND
1
Research Products
PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
7
Research Products
Documents

80

Citations

1231

h-index

20

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Documents

74

Citations

1079

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Scholarly Output

70

Articles

58

Views / Downloads

75/170

Supervised MSc Theses

4

Supervised PhD Theses

4

WoS Citation Count

438

Scopus Citation Count

511

Patents

0

Projects

0

WoS Citations per Publication

6.26

Scopus Citations per Publication

7.30

Open Access Source

34

Supervised Theses

8

JournalCount
Computational Economics6
Applied Economics5
Mathematics3
Springer Proceedings in Business and Economics -- 4th International Conference on Banking and Fice Perspectives, ICBFP 2019 -- 2 May 2019 through 3 May 2019 -- Famagusta -- 2737293
Environmental Modeling & Assessment2
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Subject: C23

Scholarly Output Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Article
    Citation - WoS: 6
    Citation - Scopus: 4
    Is real per capita state personal income stationary? New nonlinear, asymmetric panel-data evidence
    (Wiley, 2020) Emirmahmutoglu, Furkan; Gupta, Rangan; Miller, Stephen M.; Omay, Tolga
    This paper re-examines the stochastic properties of U.S. state real per capita personal income, using new panel unit-root procedures. The new developments incorporate non-linearity, asymmetry, and cross-sectional correlation within panel-data estimation. Including nonlinearity and asymmetry finds that 43 states exhibit stationary real per capita personal income whereas including only nonlinearity produces 42 states that exhibit stationarity. Stated differently, we find that two states exhibit nonstationary real per capita personal income when considering nonlinearity, asymmetry, and cross-sectional dependence.
  • Thumbnail Image
    Article
    Citation - Scopus: 2
    The Refinement of a Common Correlated Effect Estimator in Panel Unit Root Testing: an Extensive Simulation Study
    (Mdpi, 2024) Omay, Tolga; Akdi, Yilmaz; Emirmahmutoglu, Furkan; Eryilmaz, Meltem
    The Common Correlated Effect (CCE) estimator is widely used in panel data models to address cross-sectional dependence, particularly in nonstationary panels. However, existing estimators have limitations, especially in small-sample settings. This study refines the CCE estimator by introducing new proxy variables and testing them through a comprehensive set of simulations. The proposed method is simple yet effective, aiming to improve the handling of cross-sectional dependence. Simulation results show that the refined estimator eliminates cross-sectional dependence more effectively than the original CCE, with improved power properties under both weak- and strong-dependence scenarios. The refined estimator performs particularly well in small sample sizes. These findings offer a more robust framework for panel unit root testing, enhancing the reliability of CCE estimators and contributing to further developments in addressing cross-sectional dependence in panel data models.