ECON 3209 — Econometrics with Python
ECON 3209 — Econometrics with Python
BSc Hons Cooperation & Banking | 6th Semester
Kerala Agricultural University · 2025–26
A 20-week hands-on journey from Python fundamentals to applied econometrics — with every lecture interactive in your browser.
Course Overview
This course equips students with the quantitative and computational skills needed to analyse economic and agricultural data. Using Python throughout, you will progress from programming basics to implementing the full suite of classical econometric models — all within an interactive environment where code runs live in your browser (no installation required).
What You Will Learn
- Python programming for data analysis
- Data wrangling and visualisation with Pandas & Matplotlib
- Ordinary Least Squares (OLS) estimation and inference
- Diagnosing and correcting regression problems
- Panel data, qualitative response, and time series models
Course Details
| Course Code | ECON 3209 |
| Credits | 3 |
| Semester | 6th |
| Programme | BSc Hons Cooperation & Banking |
| University | Kerala Agricultural University |
| Duration | 20 Weeks × 3 Lectures |
| Total Lectures | 60 |
Course Structure
🐍 Phase 1 — Python Foundations (Weeks 1–6)
Week 1 · Python Environment & Basics
Getting started with Python · Variables & data types · Operators & expressions
Week 2 · Control Flow & Functions
Conditionals & loops · Functions & scope · List comprehensions & lambda
Week 3 · NumPy for Economics
Arrays & vectorisation · Linear algebra · Statistical operations
Week 4 · Pandas & Data Wrangling
Series & DataFrames · Merge / reshape / groupby · Real agricultural data
Week 5 · Data Visualisation
Matplotlib foundations · Seaborn statistical charts · Interactive plots
Week 6 · Working with Real Data
Import / export & APIs · Exploratory data analysis · Data cleaning case study
📊 Phase 2 — Econometrics (Weeks 7–20)
Week 7 · Introduction to Econometrics
What is econometrics? · Economic data types · Simple OLS derivation
Week 8 · OLS: Theory & Properties
Gauss-Markov theorem · Statistical properties · Python OLS from scratch
Week 9 · Inference in OLS
Hypothesis testing · Confidence intervals · t and F tests in Python
Week 10 · Multiple Linear Regression
MLR setup · Interpretation · Partial effects & Python implementation
Week 11 · Multicollinearity
Detection · Consequences · Remedies & VIF in Python
Week 12 · Heteroscedasticity
Nature & consequences · Detection tests · WLS & robust standard errors
Week 13 · Dummy Variables
Qualitative predictors · Interaction terms · Structural breaks in Python
Week 14 · Nonlinear Regression
Log & polynomial models · Splines · Python curve fitting
Week 15 · Qualitative Response Models
Linear probability model · Logit · Probit in Python
Week 16 · Model Specification
Omitted variable bias · RESET test · AIC / BIC model selection
Week 17 · Panel Data Analysis
Pooled vs fixed vs random effects · Hausman test · Python implementation
Week 18 · Autocorrelation
Nature & causes · Durbin-Watson & Breusch-Godfrey tests · Cochrane-Orcutt
Week 19 · Time Series: Basics
Stationarity & unit roots · ACF / PACF · ADF test in Python
Week 20 · Time Series: Forecasting
ARIMA models · Forecast evaluation · Course review
Interactive Slides: All lecture slides include live Python code cells — run Python directly in your browser, no installation needed.