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Journals / Ekoist: Journal of Econometrics and Statistics / 2025 / Sayı: 43

Determinants of Turkish Airport Revenues Using Spatial Panel Regression Models

Arzu Çubukçu(HACETTEPE UNIVERSITY)0000-0002-0744-4056
Semra Türkan(HACETTEPE UNIVERSITY)0000-0002-4188-184X
Pages
18–33
Publication Date
2025-12-26
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the findings of the SDM model, the number of passengers had a statistically significant and positive effect on terminal revenues, and the number of foreign visitors in neighbouring cities had a statistically significant but negative effect on terminal revenues.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, the spatial effects of aviation data and economic variables on airport terminal revenues in Türkiye were investigated using spatial panel data models.
📊
Key Findings & Data:Based on previous studies, the variables that are thought to affect terminal revenues were as follows: number of passengers, cargo volume, annual exchange rate, real gross domestic product (GDP), and number of foreign visitors.
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Conclusion & Impact:According to the findings of the SDM model, the number of passengers had a statistically significant and positive effect on terminal revenues, and the number of foreign visitors in neighbouring cities had a statistically significant but negative effect on terminal revenues.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Aviation Data · Spatial Panel Models · Moran’s I

Abstract

In this study, the spatial effects of aviation data and economic variables on airport terminal revenues in Türkiye were investigated using spatial panel data models. This study is the first study to model airport revenues in Türkiye spatially. Based on previous studies, the variables that are thought to affect terminal revenues were as follows: number of passengers, cargo volume, annual exchange rate, real gross domestic product (GDP), and number of foreign visitors. Four different spatial panel data models were applied in the analysis: Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), Spatial Autoregressive Combined Model (SAC) and Spatial Durbin Model (SDM). Statistical tests and model fit criteria were used to determine the most appropriate model. The Hausman test was performed to decide between fixed and random effects, and the Akaike information criterion (AIC) and Bayesian information criterion (BIC) were evaluated to compare model performances. Because of the Hausman test, fixedeffects models were more appropriate than random-effects models. Among the fixed effects models, the SDM model was determined to be the best fit according to the AIC and BIC values. According to the findings of the SDM model, the number of passengers had a statistically significant and positive effect on terminal revenues, and the number of foreign visitors in neighbouring cities had a statistically significant but negative effect on terminal revenues.

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Cite This Article

Çubukçu, A., Türkan, S. (2025). Determinants of Turkish Airport Revenues Using Spatial Panel Regression Models. Ekoist: Journal of Econometrics and Statistics, Sayı: 43, 18–33. https://doi.org/10.26650/ekoist.2025.43.1519393

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