Python for Econometrics
Python for Econometrics: From Theory to Real-World Applications is a comprehensive course designed to help you master econometric concepts and apply them using Python in a practical and intuitive way. This course takes you from the fundamentals of Python programming to advanced econometric techniques, making it suitable for beginners as well as learners with some prior knowledge of economics or data analysis. You will learn how to perform regression analysis, interpret results, and understand the underlying assumptions of econometric models. The course also covers important diagnostic tests such as multicollinearity, heteroscedasticity, and autocorrelation, along with their remedies, ensuring you can build reliable and robust models.
What you’ll learn
- Perform regression on python.
- Identify and Remove violations of classical linear regression assumptions.
- Master Time Series Model like ARIMA, VAR, GARCH.
- Use of econometrics model for forecasting and decision-making.
Course Content
- Introduction –> 2 lectures • 10min.
- Python Programming Basics –> 3 lectures • 52min.
- Regression Analysis –> 2 lectures • 31min.
- Functional Forms and Regression –> 4 lectures • 23min.
- Dummy Variable and Regression –> 3 lectures • 39min.
- Multicollinearity –> 3 lectures • 33min.
- Heteroscedasticity –> 6 lectures • 49min.
- Autocorrelation –> 6 lectures • 33min.
- Qualitative Regression Model –> 2 lectures • 24min.
- Times Series Econometrics-Basics –> 5 lectures • 52min.
- Time Series -ARIMA –> 4 lectures • 29min.
- Time Series VAR –> 3 lectures • 25min.
- ARCH and GARCH Models –> 2 lectures • 17min.
Requirements
Python for Econometrics: From Theory to Real-World Applications is a comprehensive course designed to help you master econometric concepts and apply them using Python in a practical and intuitive way. This course takes you from the fundamentals of Python programming to advanced econometric techniques, making it suitable for beginners as well as learners with some prior knowledge of economics or data analysis. You will learn how to perform regression analysis, interpret results, and understand the underlying assumptions of econometric models. The course also covers important diagnostic tests such as multicollinearity, heteroscedasticity, and autocorrelation, along with their remedies, ensuring you can build reliable and robust models.
In addition, you will explore advanced topics including logit and probit models, panel data analysis using fixed and random effects, and time series techniques such as ARIMA, VAR, and GARCH models. Special emphasis is placed on real-world applications, where you will work with datasets to perform forecasting, conduct Granger causality tests, analyze impulse response functions (IRF), and understand forecast error variance decomposition (FEVD). The course is structured to balance theory with hands-on coding using popular Python libraries like NumPy, Pandas, Statsmodels, and Matplotlib. By the end of the course, you will have the skills and confidence to build, interpret, and apply econometric models for academic research, professional projects, and data-driven decision-making.