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MPhil in Economic Research - Core Modules

Core Modules

  • E100 : Principles of Microeconomics I

    This course will cover the standard economic models of individual decision-making with and without uncertainty, models of consumer behaviour and producer behaviour under perfect competition and the Arrow-Debreu general equilibrium model.

  • R101 : Microeconomics II

    This course aims to familiarise students with the basic tools of (mainly non-co-operative) game theory and to enable them to apply game-theoretic-skills to simple economic problems all by themselves. The course will be concerned with both static and dynamic games.

  • R200 : Principles of Macroeconomics II

    This course covers the methodological foundations of modern macroeconomics. The emphasis is put on a rigorous and mathematical treatment of macroeconomic issues, covering concepts such as optimization, recursivity, and competitive equilibria.

  • R201 : Macroeconomics II

    This course uses the ideas introduced in Prinicples of Macroeconomics II in order to analyse macroeconomic problems. The course provides a modern treatment of canonical macroeconomic models.

  • R300 : Econometrics I

    This module serves as an introduction to fundamental econometric techniques at the graduate level. We will broadly cover maximum likelihood, linear and nonlinear regression, generalized method of moments, instrumental variables, linear time series processes, and linear models for panel data.

  • R301 : Econometrics II

    The time series part of the course will show how economic and financial time series can be modelled and analysed. Topics covered include ARMA models, state space models, trends and cycles, multivariate models and co-integration, nonlinear models and changing volatility.

    The cross-section and panel data part of the course will cover a broad range of topics in microeconometrics. Topics covered will be taken from random utility models in discrete choice, heterogeneity and endogeneity in binary choice models, program evaluation and treatment effects, fixed and random effects estimators for panel data, nonlinear and dynamic panel data models, machine learning, count data models, and an introduction to simulation methods (classical and Bayesian).