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Econometrics techniques for data science
Methods, models, tools and business solutions
I wrote an article a while ago about econometrics (Econometrics 101 for Data Scientists). The article resonated well with readers, but that was a kind of introductory article for data science people who might not be otherwise familiar with the domain.
Inspired by the response to that article, today I’m attempting to take it to the next level by making it a bit comprehensive. I’ll mostly focus on the methods, tools, and techniques used in econometrics that data scientists will benefit from.
What is econometrics
Econometrics is a sub-domain of economics that applies mathematical and statistical models with economic theories to understand, explain and measure causality in economic systems.
With econometrics, one can make a hypothesis that the length of education has a positive impact on wage rates; then qualify this relationship with economic theory; and finally, formalize that relationship quantitatively (e.g. 1 additional year of schooling increases wage by 5%) using mathematical and statistical techniques (e.g. regression). A couple of other examples are:
- Predicting spatial dependence between commercial and residential mortgage defaults using time series data
- Measuring the sensitivity of gasoline consumption to a change in the market price
The econometrics domain largely deals with macro-economic phenomena such as employment, wage, economic growth, environment, agriculture and inequality, but these principles are equally applicable in solving business and machine learning problems.
Econometrics Methods
There is no clear-cut boundary within which econometrics operates, so it is difficult to list all the methods, tools and techniques that fall within it. Keeping that in mind, and since I’m writing this article for data scientists, I broadly grouped econometrics methods into four categories: descriptive statistics, hypothesis testing, regression and forecasting.
Let’s do a deeper dive into each category.
