Multivariate Time-Series Forecasting of Youth Unemployment in Turkey: A Comparison of Deep Learning and Econometric Models

Youth unemployment remains one of the most persistent and structurally sensitive challenges in emerging economies, particularly in environments characterized by macroeconomic volatility and frequent shocks. This study investigates the dynamics and forecasting performance of youth unemployment in Turkey by adopting a symmetry-based multivariate framework that explicitly contrasts equilibrium-oriented and asymmetric temporal behaviors. Using monthly data covering the period 2009–2024, youth unemployment is modeled jointly with key macroeconomic indicators, including economic growth, inflation, overall unemployment, labor force participation, migration, exchange rates, and consumer confidence. The empirical strategy integrates traditional econometric models and modern machine learning approaches under a unified and leakage-free evaluation protocol. Stationarity and long-run properties of the series are examined using unit root tests and the Bayer–Hanck cointegration approach, followed by long-run coefficient estimation via FMOLS and DOLS. Forecasting performance is then compared across VARIMA, Prophet, and deep learning models (RNN, LSTM, and GRU), including both vanilla and hyperparameter-tuned specifications. The results reveal a clear performance hierarchy. VARIMA models, particularly the VARIMA (p = 2, q = 0) specification, consistently outperform all alternatives by a wide margin, achieving exceptionally low forecast errors. This finding indicates that youth unemployment in Türkiye is predominantly governed by symmetric co-movements and long-run equilibrium relationships among macroeconomic variables. Prophet and GRU models capture short-term and regime-sensitive fluctuations more flexibly, reflecting asymmetric temporal responses, but at the cost of higher forecast dispersion. In contrast, RNN and LSTM models exhibit limited generalization capability and are prone to overfitting in the small-sample macroeconomic context. As a result, this study positions the estimation of youth unemployment as both an econometric challenge and a symmetry-based analytical problem, offering new methodological and conceptual insights consistent with a fresh perspective.