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MPhil in Economic Research - Core Modules

Core Modules

  • R100 : Microeconomics

    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.
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  • 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 economic problems all by themselves.
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  • R200 : Advanced Macroeconomics

    The course provides the foundations and core topics of advanced macroeconomic theory that is used for macroeconomic research. It includes techniques (e.g. definition of general equilibrium, elements of dynamic programming and optimal control theory, etc.) and two broad macroeconomic topics, economic growth and business cycle theory (in the context of DSGE modelling).
    Course outline

  • R201 : Advanced Macroeconomics II

    There are two main goals of this course. Firstly, to develop students' skills at formulating and analyzing recursive competitive equilibrium models that feature heterogeneity and frictions. Secondly, to introduce students to some of the issues that these benchmark models have been used to address.
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  • R300 : Advanced Econometric Methods

    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.
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  • One of either:

    R301a : Econometrics II: Time Series

    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.
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    R301b : Econometrics II: Cross Section and Panel Data

    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).
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