Omay, Tolga

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

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5

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0

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14

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2

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10

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2

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3

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2

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2

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9

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3

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16

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11

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1

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8

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11

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13

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7

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4

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6

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80

Citations

1218

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20

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74

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Scholarly Output

65

Articles

55

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19/0

Supervised MSc Theses

4

Supervised PhD Theses

2

WoS Citation Count

435

Scopus Citation Count

503

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13

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13

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0

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0

WoS Citations per Publication

6.69

Scopus Citations per Publication

7.74

Open Access Source

32

Supervised Theses

6

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Applied Economics5
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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
Mathematics3
Environmental Science and Pollution Research2
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Has files: trueSubject: Cross-Sectional Dependency

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    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.