[last updated in 6th Jan 2022]
These lecture notes are intended for introductory econometrics course (originally used for new-hire training in the hedge fund that I was working in), suitable for university/grad students, data/quantitative analysts, junior business/economic/financial researchers and etc.
The lectures notes are loosely based on several textbooks:
- Introduction to Econometrics, by Christopher Dougherty
- Introduction to Econometrics, by James H. Stock and Mark W. Watson
- Basic Econometrics, by Damodar N. Gujarati
Though the lectures are introductory level, it would be ideal that attendants have a slight exposure to probability theory and statistics.
And you would benefit more from the tutorials if you have basic knowledge of:
- NumPy
- Matplotlib
- Pandas
I strongly suggest to download all the files to view them on your PC, since nbviewer and Github has frequent rendering glitches.
Lecture 1 - Simple Linear Regression
Lecture 2 - Multiple Linear Regression, Multicollinearity and Heteroscedasticity
Lecture 3 - Practical Cases of Linear Regression
Lecture 4 - Dummy Variables
Lecture 5 - Nonlinear Regression
Lecture 6 - Qualitative Response Model
Lecture 7 - Model Specification
Lecture 8 - Identification and Simultaneous-Equation Models
Lecture 9 - Panel Data Analysis
Lecture 10 - Autocorrelation
Lecture 11 - Time Series: Basics
Lecture 12 - Time Series: Forecast
This set of notes are rewritten from my MATLAB econometrics notes, which are outdated. I am still organizing the old materials
