Analysis Factors Affecting Egyptian Inflation Based on Machine Learning Algorithms

Mohamed F. Abd El-Aal; Mohamed F. Abd El-Aal

doi:10.3934/DSFE.2023017

Data Science in Finance and Economics

2023, Volume 3, Issue 3: 285-304. doi: 10.3934/DSFE.2023017

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Analysis Factors Affecting Egyptian Inflation Based on Machine Learning Algorithms

Mohamed F. Abd El-Aal ^,

Economics department, Faculty of Commerce, Arish University, North Sinai, Egypt

Received: 23 April 2023 Revised: 08 August 2023 Accepted: 13 August 2023 Published: 01 September 2023
JEL Codes: C80, C81, C87

Given that inflation is one of the most important problems facing the Egyptian economy, determining the factors affecting it is very important. Thus, we aim to use machine learning algorithms like Support vector machine (SVM), K-nearest neighbor (KNN), Random Forest (R.F.), Neural Network (ANN), Gradient boosting (G.B.) and decision tree (D.T.) to determine the accurate algorithm and analyze the factors affecting on Egypt inflation. The study found that the G.B. algorithm is the most accurate among the used algorithms and showed that The major significant variables determining inflation in Egypt are the exchange rate (30.5%), gross fixed formation (24.5%) and government expenditure (12.3%). We also found a positive relationship between the inflation rate and government expenditure, money supply, gross domestic product (GDP) growth, gross fixed formation, foreign direct investment, GDP per capita and exchange rate. Furthermore, there is a negative relationship between the inflation rate, household expenditure and the external trade balance.
- inflation rate forecasting,
- mmachine learning,
- nneural network,
- gradient boosting,
- decision tree
Citation: Mohamed F. Abd El-Aal. Analysis Factors Affecting Egyptian Inflation Based on Machine Learning Algorithms[J]. Data Science in Finance and Economics, 2023, 3(3): 285-304. doi: 10.3934/DSFE.2023017

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Abstract

Given that inflation is one of the most important problems facing the Egyptian economy, determining the factors affecting it is very important. Thus, we aim to use machine learning algorithms like Support vector machine (SVM), K-nearest neighbor (KNN), Random Forest (R.F.), Neural Network (ANN), Gradient boosting (G.B.) and decision tree (D.T.) to determine the accurate algorithm and analyze the factors affecting on Egypt inflation. The study found that the G.B. algorithm is the most accurate among the used algorithms and showed that The major significant variables determining inflation in Egypt are the exchange rate (30.5%), gross fixed formation (24.5%) and government expenditure (12.3%). We also found a positive relationship between the inflation rate and government expenditure, money supply, gross domestic product (GDP) growth, gross fixed formation, foreign direct investment, GDP per capita and exchange rate. Furthermore, there is a negative relationship between the inflation rate, household expenditure and the external trade balance.

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