This course deals with the economics of development, focusing on both the theoretical debates and experiences of economic transformations from various parts of the world. It aims to equip the students with an understanding of structural transformation, development planning, agriculture-industry linkages, industrialization debates, and international trade and finance. Students will also be made familiar with issues of poverty, distribution, and social development

This module introduces students in economics and other social sciences to game theory, a theory of interactive decision making. This module provides students with the basic solution concepts for different types of non-cooperative games, including static and dynamic games under complete and incomplete information. The basic solution concepts that this module covers are Nash equilibrium, subgame perfect equilibrium, Bayesian equilibrium, and perfect Bayesian equilibrium. This module emphasizes the applications of game theory to economics, such as duopolies, bargaining, and auctions.

This course will introduce students to advanced topics in Economics as decided by the instructor.

This is an advanced level course in trade. The objective of this course would be to give adequate training to graduate students in economics (and related disciplines) to bring them to the forefront of knowledge in international trade and prepare them to undertake independent work in the areas of trade theory and policy.

Economics has a significant component of experimentation and data analytics. This course covers advanced topics of economics laboratory course as a continuation of economics laboratory I course. We cover experimental methods and computational techniques using real life data. Data analysis and experimental methods of analysis feature prominently in this course.

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.

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.

This course will cover time series econometrics and forecasting with focus on applications in macroeconomics and international finance. It introduces students to time series regression models, its estimation, inference and forecasting. The objective of the course is for students to get hands-on experience with analyzing economic time series data. Accordingly, the emphasis of the course is on empirical applications.

This forms the second part of two-part game theory course intended for the masters program at IIT Delhi. After formalization of standard game theory models and some examples in game theory 1, in this course, we introduce concepts of cooperative game theory and advanced topics such as mechanism design and implementation theory. After studying this course the students are expected to be comfortable in reading the journal articles on game theory, mechanism design, and implementation theory and also develop fresh models to close the gaps in the existing literature or ideate problems in these fields.

Estimation and inference in two variable model; OLS assumption; Extension of the two variable model; OLS assumption : autocorrelation, multicollinearity, and heteroskedasticity, models with limited dependent variables : LPM, log it, and probit; Panel data modelling: fixed effect and random effect models; Time series analysis : introduction to non-stationarity, AR and MA modelling.