Machine learning methods in evaluating the impact of economic factors on the consumer price index in Albania

Abstract

The Consumer Price Index (CPI) in Albania is a measure of inflation that tracks changes in the prices of a basket of goods and services typically purchased by urban households in the country. It is a vital economic indicator used to assess changes in the cost of living and the overall price level in Albania. There are several factors that affect the levels and progress of the CPI, among them we have chosen: Euro/Lek and USD/Lek exchange rates, import levels, the monetary base, and salary data, from January 2007 to September 2023. In this paper, we investigate the efficiency of machine learning methods in determining the factors that have the greatest impact on the CPI. In our analysis, we assess the effectiveness of decision-tree models, Random Forest and XGBoost algorithms, in predicting the CPI behavior in Albania. Based on our empirical findings, we conclude that the monetary base and wages play a crucial role in influencing the CPI, with imports and exchange rates following closely in significance. Additionally, our results indicate that the Random Forest model demonstrates superior accuracy and demands less parameter tuning time compared to the alternatives. This research underscores the critical role of model selection in achieving precision and dependability in CPI forecasting. It underscores the immense potential of machine learning models in enhancing forecasting accuracy. The implications of this study are significant, as they can foster the creation of more precise and dependable forecasting models, equipping policymakers with a deeper understanding of economic stability.