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    A historical and econometric investigation of housing bubbles in OECD countries: insights from the GSADF test and machine learning
    (Springer, 2025) Ozgur, Onder; Yilanci, Veli
    This paper investigates the existence, causes, and predictive markers of housing price bubbles in 18 OECD countries from 1870 to 2020, thus addressing a significant gap in the understanding of housing market dynamics and their implications for global financial stability. Housing bubbles have substantial impact on economic resilience and have his-torically led to severe financial crises. Employing the Generalized Supremum Augmented Dickey-Fuller (GSADF) test, this study identifies multiple bubble episodes in Australia, Denmark, Germany, Japan, Portugal, and the United States. Furthermore, by using an advanced machine learning approach, Extreme Gradient Boosting (XGBoost), this study statistically confirms the significance of interest rates, loan growth, and population growth as key predictors of housing bubbles. The findings indicate that interest rate variables are the predominant predictors, explaining over 60% of bubble dynamics in Australia and Japan, whereas credit growth and demographic factors are more influential in predicting bubbles in Germany, Denmark, and the United States. This study's originality lies in its comprehensive integration of econometric and machine learning methodologies, offer-ing more accurate, data-driven detection and prediction of housing bubbles than previ-ous research. The study's findings underscore the necessity of coordinated monetary and macroprudential policies, along with proactive demographic and credit market manage-ment, to mitigate future bubble-related risks, presenting significant implications for global policymakers and market participants.
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    Are Shocks to the Grazing Land Footprint Permanent or Transitory? Evidence from a Machine Learning-Based Unit Root Test
    (Mdpi, 2025) Yilanci, Veli; Ozgur, Onder; Saritas, Merve Mert
    Understanding the dynamic behavior of the grazing land footprint (GLF) is critical for sustainable land management. This study examines the GLF in 92 countries to determine if the series is stationary, a statistical property indicating that shocks have transitory effects, or non-stationary, which implies that shocks have permanent, cumulative impacts (a phenomenon known as persistence). We employ a novel machine learning framework that uses an XGBoost algorithm to synthesize information from multiple conventional tests and time-series characteristics, enhancing analytical robustness. The results reveal significant cross-country heterogeneity. The GLF exhibits stationary behavior in a subset of nations, including China, India, and Norway, suggesting that their ecosystems can absorb shocks. However, for most countries, the GLF is non-stationary, indicating that ecological disruptions have lasting and cumulative impacts. These findings underscore that a one-size-fits-all policy approach is inadequate. Nations with a stationary GLF may find short-term interventions effective, whereas those with non-stationary series require profound structural reforms to mitigate long-term degradation. This highlights the critical role of advanced methodologies in shaping evidence-based environmental policy.
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    Nuclear energy consumption and CO2 emissions in India: Evidence from Fourier ARDL bounds test approach
    (Korean Nuclear Soc, 2022) Ozgur, Onder; Yilanci, Veli; Kongkuah, Maxwell
    This study uses data from 1970 to 2016 to analyze the effect of nuclear energy use on CO2 emissions and attempts to validate the EKC hypothesis using the Fourier Autoregressive Distributive Lag model in India for the first time. Because of India's rapidly rising population, the environment is being severely strained. However, with 22 operational nuclear reactors, India boasts tremendous nuclear energy potential to cut down on CO2 emissions. The EKC is validated in India as the significant coefficients of GDP and GDP.2 The short-run estimates also suggest that most environmental externalities are corrected within a year. Given the findings, some policy recommendations abound. The negative statistically significant coefficient of nuclear energy consumption is an indication that nuclear power expansion is essential to achieving clean and sustainable growth as a policy goal. Also, policymakers should enact new environmental laws that support the expansion and responsible use of nuclear energy as it is cleaner than fossil fuels and reduces the cost and over-dependence on oil, which ultimately leads to higher economic growth in the long run. Future research should consider studying the nonlinearities in the nuclear energy-CO2 emissions nexus as the current study is examined in the linear sense. (c) 2021 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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    Testing Real Interest Rate Parity for EU5 Countries: 200 Years of Data, Non-normality, Non-linearity and Breaks
    (Prague Univ Economics And Business, 2025) Yilanci, Veli; Ozgur, Onder
    Purpose: This paper aims to examine the real interest rate parity (RIP) theory for EU5 countries (France, Germany, Italy, Spain and the UK) versus the USA. Design/methodology/approach: Utilizing RALS-FADF and RALS-FKSS unit root tests, this study addresses non-normality, non-linearity and structural breaks in real interest rate differentials. Findings: The results confirm the RIP theory, indicating mean reversion of real interest rate differentials and highlighting impact of financial integration on monetary policy independence and arbitrage opportunities. The study notes that central banks' ability to influence domestic economies through interest rates is limited due to global financial interconnectedness. Originality/value: The paper offers a new test and bases its empirical setup on whether interest rate differentials are non-normally distributed. The test also considers real interest rate non-linearity and the non-normality in the analysis.
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    THE DEPENDENCE OF CLEAN ENERGY STOCK PRICES ON THE OIL AND CARBON PRICES: A NONLINEAR PERSPECTIVE
    (Editura Ase, 2022) Yilanci, Veli; Ozgur, Onder; Altinsoy, Abdulkadir
    Climate change, rising environmental concerns increased scholar's awareness of the complex ties between clean energy stock prices and various environmental indicators. A clearer understanding of the potential ties between indicators and clean energy stock prices is critical for determining the financial performance of clean energy companies. This study adds to the literature by testing the existence of the long-run relationship between clean energy stock prices, and oil prices, carbon prices, technology stock prices, and interest rates by considering nonlinearity in the context of a structural change. The results show the existence of the cointegration relationship. The results of long-run estimation show that before the structural break date, technology stock prices, oil prices, and interest rates positively affect clean energy stock prices, and after this date, the effects of carbon prices and interest rates are reversed. Our results present some implications for both investors and policymakers.