Course Review

ECON 3209 · Week 20, Lecture 3 · Kerala Agricultural University

Department of Development Economics, KAU

Autumn 2026

Learning Outcomes

By the end of this lecture, you should be able to:

  1. review the full 20-week structure of ECON 3209
  2. summarise the most important econometric formulas and workflows from the course
  3. run a compact Python mini-project that combines description, estimation, diagnostics, and forecasting
  4. identify next steps for further study and research after this course
  5. leave the course with a clear roadmap for independent practice

Full Course Map

Week Topic
1 Python Environment & Basics
2 Control Flow & Functions
3 NumPy for Economics
4 Pandas & Data Wrangling
5 Data Visualisation
6 Working with Real Data
7 Introduction to Econometrics
8 OLS: Theory & Properties
9 Inference in OLS
10 Multiple Linear Regression
Week Topic
11 Multicollinearity
12 Heteroscedasticity
13 Dummy Variables
14 Nonlinear Regression
15 Qualitative Response Models
16 Model Specification
17 Panel Data Analysis
18 Autocorrelation
19 Time Series: Basics
20 Time Series: Forecasting

The Core Workflow of Applied Econometrics

  1. Start with an economic question.
  2. Obtain and clean data.
  3. Visualise and describe the sample.
  4. Choose an econometric model linked to theory.
  5. Estimate, diagnose, and improve the specification.
  6. Interpret results for policy, management, or research.
  7. Forecast or simulate when needed.

Key Formula Sheet

  • OLS slope intuition: partial effect holding other regressors constant.
  • Omitted variable bias:

\[\text{plim}\hat{\beta}_1 = \beta_1 + \beta_2 \frac{\text{Cov}(X_1,X_2)}{\text{Var}(X_1)}\]

  • Logit probability:

\[P(Y_i=1|X_i) = \frac{e^{\beta_0+\beta_1 X_i}}{1+e^{\beta_0+\beta_1 X_i}}\]

More Formulas to Remember

  • Fixed effects within estimator:

\[\ddot{y}_{it} = \ddot{x}_{it}'\beta + \ddot{\varepsilon}_{it}\]

  • Durbin-Watson:

\[DW = \frac{\sum_{t=2}^T(\hat{e}_t - \hat{e}_{t-1})^2}{\sum_{t=1}^T \hat{e}_t^2}\]

  • ARIMA:

\[\phi(B)(1-B)^d Y_t = \theta(B)\varepsilon_t\]

Mini-Project: Build a Dataset

Mini-Project: Describe the Data

Mini-Project: Estimate OLS

Mini-Project: Estimate Logit

Mini-Project: Forecast a Series

What’s Next?

Possible next steps after ECON 3209: - MSc courses in econometrics, statistics, agricultural economics, or data science. - Research projects on credit, prices, crop productivity, labour, and climate shocks. - Internship work with banks, cooperatives, NGOs, and policy institutions. - Independent replication of published papers using Python.

Research Applications

  • Evaluate cooperative credit programmes.
  • Model technology adoption by farmers.
  • Forecast commodity prices and procurement needs.
  • Measure policy effects with panel data and qualitative response models.
  • Extend these tools into dissertations, term papers, and evidence-based policy work.

Exercise

Using the mini-project starter, add one more explanatory variable or forecast comparison. Then explain which tool from the course helps you most: OLS, logit, diagnostics, panel methods, or time series.

Summary

  • ✅ The course moved from Python foundations to core econometric tools and finally forecasting methods.
  • ✅ You now have a workflow for data cleaning, visualisation, estimation, diagnosis, and interpretation.
  • ✅ The most important habit is to connect methods to economic questions and data structure.
  • ✅ With continued practice, these tools can support exams, projects, research, and professional work.

Thank You

ECON 3209 · Econometrics with Python
Department of Development Economics
Kerala Agricultural University

  • Thank you for your energy, questions, and coding effort across 20 weeks.
  • Keep practising with real Kerala data: crop yields, prices, credit, and rural livelihoods.
  • Best wishes for your exams, projects, and future research.