Economics PhD program

This unit uses a wide variety of econometric tools based on statistics and mathematics to analyse economic and financial concepts. It provides the techniques required to quantify the strength and form of relationships among variables of interest, and the strategies needed in order to use these techniques effectively. 

More specifically, the unit covers the theory of classical linear regression model, and other estimation methods including IV, GMM and maximum likelihood. The second half of the unit will focus on special topics in micro-econometric modelling and time series analysis.

The unit is of an advanced level, and is both theoretical and applied in nature. Assessment consists of both theoretical derivations of econometric theory and empirical analysis of real data examples. 

By the end of their studies, students will understand how to use a range of econometric models for the analysis of cross sectional and time series data, and will have the ability to conduct independent investigations to research questions in economics and finance.

This unit aims for a rigorous introduction and training of students to frontline research topics in the realm of Microeconomics.

This training will be essential for students to be able to help choose a thesis area. Additionally, the unit will provide students with the skills and information to write theoretical models and solve those systematically using existing concepts and techniques.

We will study individual decision making with and without uncertainty, general equilibrium, collective decision-making, and game theory.

Topics will include:

• consumer choice
• demand theory
• production
• choice under uncertainty
• general equilibrium
• social choice
• games of complete information.

We will present economic applications of these tools and concepts and introduce students to a number of key articles in the related literature.

The purpose of this unit is to develop foundation knowledge in the area of Macroeconomics. The unit provides an introduction to neoclassical macroeconomics in a closed economy.

Students will acquire the knowledge of the neoclassical model, simulate it in Matlab, Dynare, and Excel, and use it as a tool to understand the aggregate behaviour of the economy.

First, we will understand the properties of the Neoclassical model. Next, we will use the model to generate time series of aggregate variables and see how such time series compare to the data. Further, we will study the effects of different policies and we will ask what optimal policies look like.

This unit is a continuation of MAE 901 – Advanced Econometrics and covers selected advanced topics most relevant for applied economics research. 

The unit is applied in nature but strongly grounded in econometric theory. Students will learn programming in the statistical software package, STATA. 

Topics will include:

• panel data
• causal analysis with experimental and observational data
• matching methods
• regression discontinuity
• Monte Carlo simulations. 

The unit is designed to develop and enhance students’ empirical skills necessary for applied research. It consists of empirical analyses of econometric models using both real and simulated data. 

Students will also be required to replicate the estimation results in articles published in leading economics journals.

This unit covers a variety of topics in macroeconomics such as monetary economics, liquidity and aggregate activity, business cycles and volatility, economic growth, income inequality, public pensions, optimal taxation and unemployment.

The topics studied all share two hallmarks of modern macroeconomic theory:

• choice over time and
• uncertainty

The unit relies heavily on the recursive approach and the basic methods of stochastic processes.

This unit covers a variety of topics in advanced microeconomic theory for T2 PhD students who have a relatively advanced knowledge of foundational microeconomic concepts and techniques, including games of complete information. 

The unit starts with a brief recap on strategic games of complete and perfect information and then introduces various structural elements of games with incomplete information. 

It introduces students to several equilibrium notions and robustness concepts. Further, students are introduced to axiomatic and non-cooperative bargaining theory and finally to various solutions and concepts of cooperative game theory. 

The unit will apply each structure and concept to various applications like auctions, tournaments, spatial competition, jury theories, strategic information transmission and allocation of resources across team members.