1. bookVolumen 12 (2022): Heft 2 (April 2022)
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
2449-6499
Erstveröffentlichung
30 Dec 2014
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch
access type Uneingeschränkter Zugang

Machine Learning and Traditional Econometric Models: A Systematic Mapping Study

Online veröffentlicht: 23 Feb 2022
Volumen & Heft: Volumen 12 (2022) - Heft 2 (April 2022)
Seitenbereich: 79 - 100
Eingereicht: 14 Jul 2021
Akzeptiert: 15 Sep 2021
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
2449-6499
Erstveröffentlichung
30 Dec 2014
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch
Abstract

Context: Machine Learning (ML) is a disruptive concept that has given rise to and generated interest in different applications in many fields of study. The purpose of Machine Learning is to solve real-life problems by automatically learning and improving from experience without being explicitly programmed for a specific problem, but for a generic type of problem. This article approaches the different applications of ML in a series of econometric methods.

Objective: The objective of this research is to identify the latest applications and do a comparative study of the performance of econometric and ML models. The study aimed to find empirical evidence for the performance of ML algorithms being superior to traditional econometric models. The Methodology of systematic mapping of literature has been followed to carry out this research, according to the guidelines established by [39], and [58] that facilitate the identification of studies published about this subject.

Results: The results show, that in most cases ML outperforms econometric models, while in other cases the best performance has been achieved by combining traditional methods and ML applications.

Conclusion: inclusion and exclusions criteria have been applied and 52 articles closely related articles have been reviewed. The conclusion drawn from this research is that it is a field that is growing, which is something that is well known nowadays and that there is no certainty as to the performance of ML being always superior to that of econometric models.

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