M.S. (Quantitative Economics)
M.S. (Quantitative Economics) Syllabus 2026
The M.S. (Quantitative Economics) (M.S. (Quantitative Economics)) syllabus covers a structured programme spanning 2 Years designed to build both foundational knowledge and specialised expertise. Below is the detailed semester-wise subject breakdown and programme structure.
M.S. (Quantitative Economics) Semester-wise Subjects
M.S. Quantitative Economics Syllabus & Subjects
The QE curriculum is designed to provide deep training in economic theory backed by rigorous mathematical and statistical methods. Core courses occupy the first three semesters, while the fourth semester is devoted to electives and a research dissertation.
Core Subjects
| Subject | Key Topics |
|---|---|
| Microeconomic Theory I | Consumer theory, producer theory, general equilibrium, welfare economics |
| Microeconomic Theory II | Game theory, mechanism design, information economics, auction theory |
| Macroeconomic Theory I | Growth theory (Solow, Ramsey), business cycles, fiscal/monetary policy |
| Macroeconomic Theory II | Dynamic macro models, expectations, DSGE models, open economy macro |
| Econometrics I | OLS, GLS, instrumental variables, panel data, maximum likelihood estimation |
| Econometrics II | Time series analysis, cointegration, VAR, GMM, limited dependent variables |
| Mathematical Methods | Real analysis, optimisation theory, fixed-point theorems, dynamic programming |
| Statistical Methods | Probability theory, estimation, hypothesis testing, Bayesian methods |
Elective Subjects (Choose 3-4)
Finance & Quantitative Methods
- Financial Economics
- Derivatives & Risk Management
- Computational Economics
- Advanced Econometrics
Economic Theory & Policy
- Development Economics
- Public Economics
- International Trade Theory
- Industrial Organisation
Applied & Empirical
- Applied Econometrics
- Labour Economics
- Health Economics
- Environmental Economics
Mathematical & Theoretical
- Game Theory (Advanced)
- Social Choice Theory
- Mathematical Finance
- Stochastic Processes
Research Dissertation
The research dissertation in Semester 4 is a defining feature of the QE programme. Students work under a faculty supervisor on an original research question, producing a 50-80 page thesis that involves:
- Literature survey and research question formulation
- Theoretical model development or empirical analysis
- Data collection and econometric estimation (if empirical)
- Written thesis with formal presentation and defence
Dissertation topics span development economics, financial economics, game theory, industrial organisation, public economics, and macroeconomic policy. Many dissertations lead to publications in peer-reviewed journals.
M.S. (Quantitative Economics) Programme Structure & Credit Distribution
M.S. QE Year-wise Curriculum
The programme follows a structured progression - Year 1 builds the theoretical and methodological foundations, while Year 2 allows specialisation through electives and culminates in a research dissertation.
Year 1 - Core Foundations
| Semester 1 | Semester 2 |
|---|---|
| Microeconomic Theory I | Microeconomic Theory II (Game Theory) |
| Macroeconomic Theory I | Macroeconomic Theory II |
| Mathematical Methods for Economics | Econometrics I |
| Statistical Methods | Indian Economy / Economic History |
Year 2 - Specialisation & Dissertation
| Semester 3 | Semester 4 |
|---|---|
| Econometrics II | Elective III / Elective IV |
| Elective I | Research Dissertation |
| Elective II | Dissertation Presentation & Defence |
Popular Specialisation Tracks
Finance Track
For careers in investment banking, quant finance, risk management:
- Financial Economics
- Derivatives & Risk Management
- Mathematical Finance
- Advanced Econometrics (Time Series)
Theory/PhD Track
For those targeting PhD programmes in economics:
- Advanced Game Theory
- Social Choice Theory
- Development Economics
- Public Economics
Applied/Policy Track
For RBI, NITI Aayog, World Bank, policy research:
- Development Economics
- Applied Econometrics
- Public Economics
- International Trade Theory
Data Science Track
For data science and analytics careers:
- Computational Economics
- Advanced Econometrics
- Stochastic Processes
- Applied Econometrics
Assessment Pattern
| Component | Weightage |
|---|---|
| Mid-Semester Examination | 25-30% |
| End-Semester Examination | 40-50% |
| Assignments & Problem Sets | 15-20% |
| Dissertation (Semester 4) | Separate evaluation - graded as a full course |
Skills Developed in M.S. (Quantitative Economics)
Skills Required & Acquired in M.S. QE
The QE programme bridges economics and quantitative methods, requiring a unique combination of skills at entry and developing a powerful analytical toolkit valued across sectors.
Skills Required Before Joining
Essential Skills
- Microeconomics: Consumer/producer theory, market structures, basic game theory at intermediate undergraduate level
- Macroeconomics: IS-LM, AD-AS, Solow growth model, monetary/fiscal policy basics
- Calculus: Multivariable calculus, constrained optimisation (Lagrange multipliers)
- Linear Algebra: Matrix operations, eigenvalues, vector spaces
- Probability & Statistics: Distributions, estimation, hypothesis testing, regression basics
Helpful Background
- Real Analysis: Sequences, continuity, compactness - helpful for mathematical methods courses
- Econometrics: OLS estimation, basic time series - gives a head start in Econometrics I
- Programming: R, Python, or Stata familiarity - increasingly useful for assignments and dissertation
- Game Theory: Nash equilibrium, extensive form games - tested in PEA/PEB
- Economic History: Indian economic development context - useful for applied courses
Skills Acquired During M.S. QE
Economic Theory
- General Equilibrium: Arrow-Debreu framework, existence and welfare theorems
- Game Theory: Mechanism design, auction theory, repeated games, Bayesian games
- Dynamic Macro: DSGE models, rational expectations, Bellman equations
- Growth Theory: Endogenous growth, Ramsey-Cass-Koopmans model
Quantitative Methods
- Advanced Econometrics: IV, GMM, panel data, time series, cointegration, causal inference
- Mathematical Economics: Fixed-point theorems, dynamic programming, optimal control
- Financial Modelling: Asset pricing, derivatives valuation, risk metrics
- Research Design: Empirical strategy, identification, robustness testing
Professional Skills
- Statistical Software: R, Stata, MATLAB, Python for econometric analysis
- Research Writing: Academic paper structure, literature review, formal argumentation
- Data Analysis: Large dataset handling, cleaning, visualisation, interpretation
- Presentation: Communicating complex economic arguments to diverse audiences