Econometric MachineLearning

Jump to: navigation, search

Introduction Econometric Vs Machine learning

This contribution, through a review of the available literature, seeks to analyze and evaluate the links between econometric and machine-learning techniques that have been researched in the context of economic and to identify areas or problems in the real-time economic prediction that have been inadequately explored and are potential areas for further research.

What is econometrics? 

Econometrics is the quantitative application of statistical and mathematical models using data to develop theories or test existing hypotheses in economics and to forecast future trends from historical data. It subjects real-world data to statistical trials and then compares and contrasts the results against the theory or theories being tested(Investopedia). In this definition, we can also distinguish:

    1. Quantitative application
    2. statistical and mathematical model
    3. input historical data
    4. Foundations: theories and hypotheses in economics
    5. Purpose: forecasting, prediction

The econometric theory concerns the development of tools and methods and the study of the properties of econometric methods. Applied econometrics is a term describing the development of quantitative economic models and the application of econometric methods to these models using economic data(Bruce E. Hansen, 2019).

What is Machine learning?

Machine learning is a method of data modelization that provides the ability to automatically learn and improve the analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

    1. data modelization
    2. automates analytical model
    3. artificial intelligence
    4. input data
    5. patterns



Theorical foundation

Modelization technique

Data analysis

Data types

Data wrangling

variables selection

Data science

Machine learning