Forecasting by Machine Learning Techniques and Econometrics: A Review

Abstract

Econometricians deal with a tremendous amount of data to derive the relationships between economic entities. When statistical techniques are applied to the economic data to determine the relative economic entities with verifiable observations, this quantitative analysis is termed Econometrics. Traditional Econometric methods employ pure statistical and mathematical concepts to analyze economic data. Applied Econometrics deals with exploring real-world observations like forecasting, fluctuating market prices, economic outcomes or results, etc. In recent years, Machine Learning models are applied to quantitative data available in almost all domains. Machine Learning Models perform very efficiently in the classification process and it is used in the field of economics to classify the economic data more accurately than traditional econometric models. In this paper, several machine learning methods that are specifically used for economic data are explored. This paper further investigates the various Supervised machine learning techniques that contribute effectively along with metrics that are involved in the analysis procedure of econometric models. This study provides deep insight into those machine learning models preferred by the Econometricians and their future implications.

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