Primary Textbooks
- Maddala, G.S. & Lahiri, K. (2009). Introduction to Econometrics (4th ed.). Wiley.
- Wooldridge, J.M. (2020). Introductory Econometrics: A Modern Approach (7th ed.). Cengage.
Course Syllabus
ECON 3209 — Econometrics with Python
Course Information
| Course Title | Econometrics with Python |
| Course Code | ECON 3209 |
| Credits | 3 |
| Programme | BSc Hons Cooperation & Banking |
| Semester | 6th Semester |
| University | Kerala Agricultural University |
| Duration | 20 Weeks (60 Lectures) |
Course Description
This course introduces students of Cooperation and Banking to the theory and practice of econometrics using the Python programming language. The first six weeks provide a thorough grounding in Python for data analysis — covering programming fundamentals, numerical computing with NumPy, data wrangling with Pandas, and scientific visualisation. The remaining fourteen weeks develop the classical econometric toolkit: from simple and multiple linear regression through diagnostic testing, qualitative response models, panel data, and time series analysis.
All lecture slides are interactive, so students can run Python code directly in their browser during class and for self-study.
Learning Outcomes
Upon successful completion of this course, students will be able to:
- Write Python programs to import, clean, and explore economic and agricultural datasets.
- Produce publication-quality statistical charts using Matplotlib and Seaborn.
- Estimate and interpret simple and multiple regression models using Python’s
statsmodels. - Diagnose violations of OLS assumptions (multicollinearity, heteroscedasticity, autocorrelation) and apply appropriate corrections.
- Model qualitative outcomes using logit and probit regression.
- Apply panel data methods (fixed and random effects) to agricultural and cooperative data.
- Test for unit roots and estimate ARIMA models for economic time series.
- Communicate econometric findings clearly in written and oral form.
Textbooks & References
Python Resources
- Sheppard, K. Introduction to Python for Econometrics, Statistics and Data Analysis. University of Oxford. Jupyter notebooks with YouTube lectures. github.com/bashtage/python-introduction
- Chen, W. Econometrics with Python. Lecture notes covering regression, panel data, and time series in Python. github.com/weijie-chen/Econometrics-With-Python
- VanderPlas, J. (2023). Python Data Science Handbook (2nd ed.). O’Reilly.
Supplementary
- Greene, W.H. (2018). Econometric Analysis (8th ed.). Pearson.
- Gujarati, D.N. & Porter, D.C. (2009). Basic Econometrics (5th ed.). McGraw-Hill.
Software
This course uses Python 3.11+ with the Anaconda distribution. All required libraries are open-source:
numpy pandas matplotlib seaborn
scipy statsmodels linearmodels jupyterNo installation needed for lectures. All interactive slide decks run Python directly in your browser.
Academic Integrity
Students are expected to adhere to the Kerala Agricultural University Academic Integrity Policy. All submitted work must be the student’s own. Discussing ideas is encouraged; copying code is not.