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Introduction to Econometrics
Okay. So what exactly is Econometrics? At its core, Econometrics refers to the application of data science to primarily economic data. Econometricians (as they are often called) use methods like quantitative models, statistical tests, and even machine learning as a way to help uncover the stories that their data may hold. In Econometrics, you learn things about your data, and when you do, the hope is that you can use what you learn today to predict what you may learn tomorrow. Confused yet? No worries. In this course, we'll take everything step by step.
Things You Are Going To Need For This Video:
Code To Follow Along
(Make Sure You Follow the Comments In the Code to Correctly Export Your Data)
Kaggle Dataset
Article: How The Granger Causality Test Works
Once you watch the videos and understand the basics behind how the Granger Causality Test works, try reading this article! The Granger Causality Test is a mathematically sophisticated concept, and it's very heavily related to the field of Quantitative Finance. This article talks about an extra-step in the whole process: checking for stationarity. Typically, you should first test for between two time series before implementing the Granger-Causality Test between those two variables. While we did not discuss stationarity in this course, this article provides you with example on how to check the stationarity between two time series in Python. We recommend that, once you have gone through the course videos on the Granger-Causality Test, you give that code a try and try to perform a stationarity test between the Singapore Dollar and Chinese Yuan to see if it would have even made sense to use the Granger Causality Test in the first place.
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