مجلة معهد العلوم الإقتصادية
Volume 28, Numéro 2, Pages 387-404
2025-12-19

Improving Econometric Models Using Artificial Intelligence And Machine Learning



Authors : Bahaz Sabina . Bedrouni Aissa .

Abstract

The most prominent applications of machine learning in economics is improving the predictive power of econometric models. Traditional econometric models are often based on a priori statistical assumptions, which limits their ability to handle complexity and uncertainty in data. These models can be combined with various techniques using machine learning techniques such as machine learning and artificial neural networks. This paper takes the case of using univariate modelling techniques to predict construction outputs, where the regression tree was used instead of the least squares method. It also explores three other univariate modelling techniques: Box-Jenkins, neural network auto regression (NNAR), and support vector machine (SVM). Furthermore, it provides an example of a regression estimation method in Python. Finally, the core feature of artificial intelligence is data processing for decision-making.

Keywords

Artificial Intelligence ; Machine Learning ; Econometrics ; Regression Tree ; Forecasting

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