Semester-wise Syllabus for M.Com in Econometrics

Semester 1: Core Foundations

1. Advanced Microeconomic Theory

• Topics: Consumer choice (utility maximization), producer theory (cost minimization), general equilibrium, game theory (Nash equilibrium).

• Tools: Lagrangian optimization, indifference curves, Cournot/Bertrand models.

• Practical: Case studies on pricing strategies (e.g., sports leagues, e-commerce).

2. Macroeconomic Modeling

• Topics: IS-LM framework, Solow growth model, New Keynesian economics, business cycle analysis.

• Tools: Dynamic stochastic general equilibrium (DSGE) basics.

• Software: Excel simulations of macroeconomic policies.

3. Mathematical Economics

• Topics: Linear algebra (matrix operations), calculus (constrained optimization), difference/differential equations.

• Application: Solving economic models like Ramsey growth.

4. Statistical Methods for Economics

• Topics: Probability distributions (normal, binomial), hypothesis testing (t-tests, ANOVA), Bayesian inference.

• Software Lab: R/Python for descriptive statistics.

5. Econometrics I: Linear Regression

• Topics: OLS assumptions, Gauss-Markov theorem, multicollinearity, heteroskedasticity.

• Practical: Stata/R exercises with real datasets (e.g., wage determinants).

Semester 2: Econometric Theory & Applications

1. Econometrics II: Violations & Remedies

• Topics: Autocorrelation, heteroskedasticity (White’s test), instrumental variables (IV), logit/probit models.

• Case Study: Impact of education on income using IV.

2. Time Series Econometrics

• Topics: Stationarity (ADF test), ARIMA, VAR, cointegration (Engle-Granger).

• Software: EViews for forecasting GDP/inflation.

3. Panel Data Analysis

• Topics: Fixed vs. random effects, Hausman test, dynamic panels (Arellano-Bond).

• Dataset: Analyzing firm performance across years.

4. Applied Econometrics Lab

• Tools: Stata/R/Python for data cleaning, regression diagnostics.

• Project: COVID-19’s economic impact using panel data.

5. Research Methodology

• Focus: Thesis design, literature review, ethical considerations.

Semester 3: Specializations & Electives

Core Subjects

1. Financial Econometrics

   • Topics: CAPM, ARCH/GARCH (volatility modeling), Value-at-Risk (VaR).

   • Software: Bloomberg Terminal/R for stock market analysis.

2. Development Econometrics

   • Topics: RCTs, difference-in-differences (DiD), propensity score matching.

   • Case: Evaluating poverty alleviation programs.

Electives (Choose 1–2)

• Behavioral Econometrics: Prospect theory applications.

• Spatial Econometrics: GIS-based regional analysis.

• Machine Learning for Economics: LASSO, random forests.

Seminar Series

• Recent papers (e.g., "Predicting recessions using Twitter data").

Semester 4: Thesis & Advanced Topics

1. Thesis Work

• Stages: Proposal defense, data collection, model estimation, viva voce.

• Examples:

  • "Cryptocurrency volatility: A GARCH approach."

  • "Gender wage gap analysis using NSSO data."

2. Advanced Topics

• Bayesian econometrics, structural equation modeling (SEM).

• Guest Lectures: Central bank economists on policy modeling.

3. Internship (Optional)

• Roles: RBI, NITI Aayog, or fintech firms.

Key Software & Tools

• Stata: Primary for econometric analysis.

• R/Python: Machine learning applications.

• EViews: Time series forecasting.

• Excel VBA: Macro-based economic models.