In the dynamic world of economic forecasting, a groundbreaking study led by Bahatdin Daşbaşı from Kayseri University has shed new light on the intricate relationships between key economic indicators in Turkey. The research, published in the Journal of Advanced Research in Natural and Applied Sciences, employs both Artificial Neural Networks (ANN) and Ordinary Differential Equations (ODE) to model the interactions among the USD opening exchange rate, the annual change rate of the Consumer Price Index (CPI), the housing loan interest rate in Turkish lira, and the residential construction cost index. The data spans from January 2015 to May 2024, sourced from the Turkish Statistical Institute (TUIK).
Daşbaşı’s study delves into the nuances of economic forecasting, offering a comparative analysis that could revolutionize how we predict and respond to economic fluctuations. “The ANN approach provided a granular view, allowing us to focus on individual variables with high precision,” Daşbaşı explained. “For instance, the model achieved an impressive 93.1% accuracy rate in predicting the construction cost index, which is crucial for the construction sector’s planning and investment decisions.”
However, Daşbaşı noted that the ODE model offered a more comprehensive framework. “The ODE model’s holistic approach captured the time-dependent relationships among all variables simultaneously, resulting in an overall accuracy of 94.6%. This method is particularly valuable for understanding the broader economic landscape and making informed decisions during periods of uncertainty.”
The implications of this research extend far beyond academia. For the energy sector, which is deeply intertwined with economic indicators, these predictive models could be game-changers. Accurate forecasting of exchange rates, CPI changes, and interest rates can help energy companies optimize their investments, manage risks, and plan for future market conditions. “Energy companies can use these models to anticipate changes in construction costs and interest rates, which are critical for their infrastructure projects,” Daşbaşı said. “This can lead to more efficient resource allocation and better strategic planning.”
The energy sector is not the only beneficiary. The construction industry, which is heavily influenced by housing loan interest rates and construction cost indices, can also leverage these models to make more informed decisions. For example, accurate predictions of construction costs can help contractors and developers plan their budgets more effectively, reducing the risk of cost overruns and delays.
This research also highlights the potential for integrating advanced mathematical models into economic forecasting. The use of ANN and ODE models demonstrates the power of combining different analytical approaches to gain a more nuanced understanding of economic dynamics. As Daşbaşı noted, “The future of economic forecasting lies in the synergy between different modeling techniques. By leveraging the strengths of both ANN and ODE models, we can develop more robust and accurate predictive frameworks.”
The study, published in the Advanced Research in Natural and Applied Sciences, marks a significant step forward in the field of economic forecasting. As we continue to navigate the complexities of the global economy, the insights provided by Daşbaşı’s research could shape future developments in predictive modeling, offering valuable tools for decision-makers across various sectors. The energy sector, in particular, stands to benefit from these advancements, enabling more strategic and data-driven approaches to investment and planning.