Available courses

Time Series Analysis and Forecasting

Global Macroeconomics

This is beginning graduate level course in open economy macroeconomics with a focus on emerging market and developing economies. This course will be conducted online. 

Analysis I

History of Economic Thought

This is a discussion class in the History of Economic Thought. There will be no lectures. Students are required to read the assigned readings before each class. Marks will be based on students' contributions to the discussions and a written final exam or term paper.

The first third of the course will cover the ideas of the classical economists beginning with Adam Smith and ending with Karl Marx. The remaining two-thirds will cover modern economic thought - 19th-century marginalism, neoclassical microeconomics, Keynesian macroeconomics, general equilibrium theory, neoclassical welfare economics, the information revolution and game theory, modern (post-1970s) macroeconomics, behavioral economics and finance, the empirical turn in economics.

 

Selected Topics I: Institutional Economics

This course deals with topics in Institutional Economics.

Advanced Topics in Industrial Organization

Incomplete Contracts and their Empirical Applications; Markups, Market Power, Collusion, Differentiated Goods and Asymmetric Information; Applications of IO methods to Developing/Transition Economies

Econometric Methods II

This course deals with methods of causal identification in empirical work.

Macroecomics II

Macro 2 covers topics in macro-dynamics. 

Economics of Education

Course Description

In this course, we will examine two broad topics:

  • How human capital (education in particular) affects the economy
  • Production function of Education

The course will examine these issues in the context of India, with its institutional constraints. The course will highlight modern tools of analyses to address important academic and policy questions in the context of education in India. We will focus more on the empirics, with the theory in the background.

Regression Techniques

Multiple linear regression; partial and multiple correlations; properties of least squares residuals; forward, backward and stepwise regression; different methods for subset selection.

Violation of linear model assumptions: Lack of fit (linearity): diagnostics and test, Model building. 

Heteroscedasticity: consequences, diagnostics, tests (including Breusch-Pagan test and White’s test) and efficient estimation. 

Autocorrelation: consequences, diagnostics, tests (including Durbin-Watson test, Breusch-Godfrey LM test and Durbin’s h-test) and efficient estimation. 

Collinearity: consequences, diagnostics and strategies (including ridge & shrinkage regression, LASSO, dimension reduction methods). 

Discordant outlier and influential observations: diagnostics and strategies. 

Robust regression techniques: LAD, LMS and LTS regression (brief exposure). 

Log-Linear models. Introduction to Generalized Linear Models (GLMs), illustration with logit and probit analysis. Linear predictor, link function, canonical link function, deviance. Maximum likelihood estimation using iteratively re-weighted least square algorithm. Goodness of fit test. 

Introduction to nonparametric regression techniques: Kernel regression, local polynomial, knn and weighted knn methods.

Data analysis and application of the above methods with computer packages.