Price fluctuations significantly impact supply and demand mechanisms, particularly in agriculture and food production. These effects are often persistent and challenging to adapt directly, making it crucial for agrarian countries to understand the factors driving these changes. This research focuses on calculating a specific food price index related to Turkish food exports, with the goal of evaluating the factors contributing to volatility in this index. Using data from 1991-2022, the analysis employed selected machine learning methodologies to project potential policy interventions.
The support vector regression (SVR) predictions revealed that rising prices of exportable products are driven by various factors, including cost items, food price inflation, unemployment levels (as an indicator of income), and exchange rates. The predictions closely aligned with the actual calculated variables, suggesting that variations in aggregate price levels, exchange rates, and technology-related and import-dependent costs are critical for observation and evaluation. These factors appear to play a more significant role in determining price inflation for Turkish agricultural and food products.
