| Sumario: | This research compares and analyzes traditional statistical methods and machine learning techniques for forecasting unemployment rates in Türkiye. Unemployment rates are affected by macroeconomic variables such as economic growth, inflation, population growth, migration movements and education expenditures. Therefore, unemployment rates are estimated using machine learning algorithms such as Random Forests (RF), Gradient Boosting (GB) and Multilayer Perceptron (MLP) based on TurkStat data and the performances of the models are compared. In the research, among the machine learning models, the MLP model showed the best forecasting performance (MAE: 1,945; RMSE: 2,235) with the lowest error rates. Although the RF and GB models achieved a certain level of accuracy, their error rates were higher compared to the MLP model. The findings suggest that machine learning techniques are more successful in unemployment forecasting than traditional statistical models. In particular, the MLP model provides more accurate forecasts than other models thanks to its capacity to learn nonlinear relationships. Moreover, correlation analyses reveal that unemployment rates are significantly correlated with inflation, economic growth and migration flows. While the negative effect of economic growth on unemployment is clearly observed, migration movements are found to increase unemployment rates. In particular, the negative correlation between inflation and education expenditures suggests that education investments decrease during periods of economic instability. In this research, unemployment rates for the years 2025, 2026 and 2027 are estimated using machine learning techniques. As a result of the analysis, the unemployment rate is expected to vary between 9.2% and 11.5% in 2025, while this rate is expected to be between 8.8% and 11.0% in 2026. According to the 2027 forecasts, the unemployment rate is expected to decline to between 8.5% and 10.7%. The results of the analysis show that the MLP model provides the closest forecasts to the TurkStat data. The RF and GB models, on the other hand, have a wider margin of error, predicting unemployment rates in the range of 9-14%. These forecasts are an important guide for economic policy makers and labor market analysts and provide predictions about the future course of unemployment rates.
|
|---|