This course is designed to introduce you to recent developments in macroeconomics. Students will learn how to formulate and solve stochastic dynamic economic models and to apply these techniques to a number of substantive issues in consumption, asset pricing, and business cycle theory. The subject will also cover Solow growth model, endogenous growth models, search models of unemployment, real business cycle theories, Mundel-Flemming and Dornbusch models, dynamic models with heterogeneous households and will use these models to analyze a range of issues.

An important part of the course will be concerned with basic tools and concepts of dynamic stochastic economic theory. We will study tools like difference equations, dynamic programming and Markov chains but most importantly we will write simple computer programs to help us solve and understand the properties of economic models that are often too complicated to be worked out “by hand”.

This course is designed for students at the doctoral level giving students an understanding of corporate finance theory and empirical evidence. The course puts significant emphasis on the interactions between capital markets and the value of the underlying real assets.
The course lays the foundation for future research in corporate finance and teaching in the field. We focus on three modules which are capital structure, dividend policy, raising capital and one sub-module focusing on event studies as an empirical corporate finance method.

This course covers statistical techniques such as descriptive analysis of data, estimation, correlation and regression analyses along with financial data and financial applications. It covers a comprehensive application of cross-section and time series financial data by also using MS Excel and EVIEWS softwares.

The course is aimed at providing the graduate students with an understanding of statistics and data analysis for managers. The course objective is to teach “the methods to be followed after collecting data for a problem to be solved”. The quality of research decisions depends to a great extent on the information available to the decision makers. Data analysis transforms raw data into information which can help in this decision making process. A manager who lacks the analytic skills to obtain insights from the available data is very much like a general manager who does not know how to read the income statement for his/her company.

This course introduces students to the time series methods and practices which are most relevant to the analysis of economic and financial time series with focus on applications in macroeconomics, international finance, and finance. We will cover univariate and multivariate models of stationary and nonstationary time series in the time domain. This course prepares you for empirical analysis towards satisfactory progress in your PhD thesis or graduate studies. The emphasis is on modelling skills and practical applications.