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MPhil in Economics - Optional Modules

Optional Modules

  • S101 : Public Economics

    This course covers the foundations for optimal taxation based largely on the seminal work of Ramsey (1927) and Mirrlees (1971). The goal of the course is to familiarize students with basic empirical methods and theoretical models in applied microeconomics, with a focus on connecting theory to data to inform economic policy. Topics include efficiency costs and incidence of taxation, inequality, optimal income and commodity taxation.

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  • S140 : Behavioural Economics

    This course offers an introduction to the behavioural approach to economics. Among the topic covered are behavioural game theory, intertemporal decision making, neuroeconomics, cognitive biases, decision-making heuristics and addiction. The course includes both theoretical and empirical material, but a recurring theme is the importance of experimental findings both in the laboratory and in the field.

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  • S150 : Economics of Networks

    The course introduces students to the economics of networks. This area of research has emerged in the last two decades and it has introduced a set of tools for economists to incorporate network structure in the analysis of individual behaviour and economic outcomes. Topics covered include the formation of networks, the provision of local public goods, coordination, learning, trading, and financial networks. A central focus of the course is the interplay between theory and experiments.

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  • S170 : Industrial Organisation

    This course provides a rigorous treatment of the main concepts in industrial organisation. The course covers both theory and applications.

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  • S201 : Applied Macroeconomics

    The aim of this course is twofold: First, it will introduce students to structural VARs, and related the concept of structural to identification more broadly. In addition, the students will obtain the computational tools to analyse structural VARs themselves using mathematical softwares (primarily Matlab). Second, the course will go through applied macroeconomic theory and analyse the effects on monetary and fiscal policy, and how the policy effectiveness are altered when the economy is in a liquidity trap.

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  • S301 : Applied Econometrics

    The aim of this module is to enable students to follow modern applied econometric papers and critically interpret empirical output, including an understanding of the limitations imposed by the econometric techniques and the data available. This course has two components (i) empirical strategies for obtaining causal estimates, including randomized experiments, difference-in-difference, regression discontinuity, selection correction and instrumental variables and (ii) panel data estimation including fixed and random effects and dynamic panel data models. The focus is on empirical rather than purely theoretical issues. The emphasis throughout will be on intuitive treatment of the issues rather than detailed technical derivations. This will include discussion of empirical papers that address policy issues via data analysis.

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  • S500 : Development Economics

    This course is on development economics and deals with the economic problems of poor countries. It considers some of the main theoretical and analytical issues in development economics as well as the historical development process of now-developed countries. The topics covered are growth, development, poverty, inequality, education, technology, innovation, mutual insurance, finance, savings, weather, climate, health, pandemics, representative democracy, religion, social capital and conflict.

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  • F500 : Empirical Finance

    This course is an introduction to some major topics in empirical finance. It aims to endow the student with an understanding of the current issues, methods, and conclusions of empirical research on financial markets. The focus is primarily on equity markets. There will be an emphasis on empirical results and their interpretation. Econometrics required background.

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  • F520 : Behavioural Finance

    The goal of the course is to better understand human attitudes towards uncertainty in general, and financial risk in particular. The method to get there is to go beyond a pure behaviouralist approach, point to the difficulty of deciphering the psychology behind behaviour, to eventually land squarely in the domain of neurobiology. The approach promises more comprehensive insights than from a purely behavioural study (which makes humans look like a bug-plagued organism), and it bypasses difficult issues of awareness and consciousness (what if people don’t know what they think or feel?). Among others, the results are: novel insights into the role of emotions; an appreciation that neurobiology provides foundations for machine learning (and how the newest in computational neuroscience may yet revolutionise machine learning); a deeper understanding as to whether and how “smart drugs” (popular among students and professionals) work.

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