I remember the first time I heard the word ‘Econometrics‘, it sounded like the most complicated thing ever. Granted I was still in college and barely understood what the field of Economics entailed. Wikipedia defines Econometrics as,
The application of mathematics, statistical methods and computer science to economics data and is described as the branch of economics that aims to give empirical content to economics relations.
Sounds easy enough right? Economics itself is also divided into two fields; Macroeconomics and Microeconomics, therefore the economic data referred to above will also differ accordingly. Econometric analysis uses real world data to assess economic theories, develop econometric models, analyse economic history and forecasting. There are many applications of this including Forecasting macroeconomic indicators such as Gross Domestic Product (GDP), Consumer Price Index (CPI) and Current Employment Statistics. Econometrics can also be applied on microeconomics data, for example, identifying factors that affect a firm’s entry and exit into a market, determining the relationship between marketing strategies and sales of a business, finding the relationship between management techniques and worker productivity, etc. And the most basic econometric model used for all this is the linear regression model (there are other more complicated models used).
There is obviously still a whole lot more about econometrics that I am yet to learn, having done only two modules in my second year of BSc Economics. But what I learnt from those two modules (Econometrics 1 and 2) was enough to peak my interest. My summer internship was econometrics based, exploring the relationship between marketing strategies and sales. And I enjoyed it so much that I am now looking forward to pursuing econometrics in my further studies. Hopefully this little insight has shed some light on the topic and maybe peaked your interest? Let me know what you think in the comments section below.
Enjoy the weekend!