The objective of this course is to provide introduction to econometric theory and practice. The linear regression model is the main focus of this course. The topics covered in the course include Gauss-Markov assumptions, Small and Large sample properties of least square estimator. Then we proceed to violation of assumptions of Gauss-Markov Theorem, which include heteroskedasticity, autocorrelation, measurement error, omitted variables and simultaneity. We also cover generalized regression models, endogeneity and instrumental variable estimation, limited dependent variable models. We also introduce basic econometric methods with time series and panel data.

Economics has a significant component of experimentation and data analytics. This course introduces students to experimental methods and computational techniques using real life data. Data analysis and experimental methods of analysis feature prominently in this course.

The course builds on microeconomic and mathematical tools to formalize decision-making by economic agents under different environments, e.g. framing, uncertainty, competitive bidding, tacit collusion etc. Starting from the basic models of consumer’s behavior, the course aims to build advanced theoretical tools to analyze behavioral aspects of physical and virtual market. Various cases (e.g. online purchase, e-auctions, spectrum auctions, tacit business-links etc.) from recent marketing literature would be analyzed with respect to decision-theoretic tools established in the course.

The aim of the course is to make the students familiar with the
most fundamental concepts and results of game theory. This forms the first part of a sequence of two courses on game theory. On successful completion of this course (and the subsequent second part of the course), the students are expected to make clear how game theory and mechanism
design can be applied to their own research. Our intention is
to present the theory with formal proofs of the main theorems including a number of
examples and short questions to enliven the presentation. The course will also include
a number of elaborate exercises, enabling the students to apply these concepts in problem settings.

The primary objective of this course is to provide an advanced treatment of econometric methods for cross section and panel data including linear and non-linear models. Some selected topics are instrumental variable estimation, estimation of system of equations, estimation of panel data models, maximum likelihood estimation, generalised method of moments, discrete response models, censored regression models and estimation of average treatment effects.

Primary objective of this course is to introduce students with the process of economic growth and the long run sources of differences in economic performances across nations. Emphasis will be placed on developing theoretical toolkit in understanding growth mechanics. It is intended that this course will make students learn some of the workhorse models in modern macroeconomics, namely - Solow-Swan model, endogenous growth models, Keynesian and heterodox models, etc.

This course will introduce students to methods of quantitative economic research using primary and secondary data source. The purpose is to provide training for Ph.D. scholars in the department.

This course is the first part of two semester long research project for M.Sc. (Economics) students. The students engage in collaborative research with a faculty guide. The objective is to prepare students in identifying research problems, formulating research questions, analysing them, and writing the final dissertation.