Microeconomics A: Game Theory

Instructor: Ruohan Qin 

Microeconomics B: Economics of Networks 

Instructor: Ruohan Qin 

This is an introductory course in game theory - the mathematical study of strategic interactions among rational decision makers. The course aims to equip you with a deep understanding of game theory’s fundamental principles, methodologies, and its applications in analysing real-world phenomena. Through a series of lectures and seminars, you will explore the theoretical foundations of game theory, including equilibrium concepts, strategic dominance, and the impacts of dynamic interactions and information incompleteness. You will progress from understanding basic concepts and models to exploring the strategic complexities of dynamic games and the uncertainties inherent in real-world interactions. 

The decisions we make are deeply influenced by the connections we maintain. These connections, whether in friendships, online platforms, supply chains, or transportation systems, are the foundation of the networks that underpin modern life. This course offers a rigorous introduction to the economics of networks, starting from foundational models and progressing to advanced models and cutting-edge research. You will explore how networks form, evolve, and influence economic and social outcomes, with applications in production and supply chains, infrastructure, social behaviour, epidemics, and networked markets. 

Macroeconomics A: Macroeconomic Theory 

Instructor: Myungun Kim 

Macroeconomics B: Monetary Policy 

Instructor: Myungun Kim

This course offers a comprehensive overview of macroeconomics, covering topics from short-run dynamics to long-run growth. Key themes include economic growth, the IS-LM framework, aggregate demand components (consumption, investment, government spending), and monetary and fiscal policy. Each lecture connects these concepts to compelling questions, such as why some countries grow richer while others falter, whether inflation can arise from expectations alone, and the limits of government debt. You will examine case studies, such as the US Federal Reserve’s recent monetary tightening to combat inflation and the UK’s fiscal responses to post-pandemic economic recovery. Designed for students transitioning from beginner to intermediate levels, the course emphasises applying macroeconomic principles to analyse contemporary challenges. 

This course provides an in-depth exploration of monetary policy frameworks, focusing on theoretical foundations and contemporary applications across major economies, including the US, UK, Europe, and key Asian economies. It examines the evolution of monetary policy from traditional interest rate rules to unconventional tools such as forward guidance and quantitative easing. The course integrates modern analytical techniques, including high-frequency data analysis and natural language processing (NLP), to assess central bank communications and policy effectiveness. 

Data Science A: Casual Inference 

Instructor: Weilong Zhang 

Data Science B: Machine Learning 

Instructor: Oleg Kitov 

This course focuses on identifying and estimating causal effects in economics and social sciences, covering randomised controlled trials, instrumental variables, difference-in-differences, fixed effects, and regression discontinuity designs. Topics include the impact of minimum wage on employment, long-term outcomes of education policies, and vaccine effectiveness. You will explore innovative tools like ChatGPT, applying AI to analyse public responses using sentiment data. Emphasizing intuition and practical application, the course bridges cutting-edge methodologies with real-world policy evaluation, equipping you with skills to interpret and address complex causal questions in contemporary economic and social contexts. 

This course introduces modern methods of supervised machine learning. It will consider data analysis models that can substantially improve prediction accuracy and interpretability compared with simple linear models, especially when faced with large datasets. Specifically, it will cover selection and shrinkage models such as Lasso, Ridge, Principal Component Analysis and Factor Models. General principles of model assessment and selection will also be discussed. The course is centred around concrete modelling applications using economic and financial datasets in Python, as you will learn valuable skills of applied data science and machine learning. 

Finance A: Financial Econometrics 

Instructor: Oleg Kitov 

Finance B: Behavioural Finance 

Instructor: Weilong Zhang 

This course focuses on identifying and estimating causal effects in economics and social sciences, covering randomised controlled trials, instrumental variables, difference-in-differences, fixed effects, and regression discontinuity designs. Topics include the impact of minimum wage on employment, long-term outcomes of education policies, and vaccine effectiveness. You will explore innovative tools like ChatGPT, applying AI to analyse public responses using sentiment data. Emphasizing intuition and practical application, the course bridges cutting-edge methodologies with real-world policy evaluation, equipping you with skills to interpret and address complex causal questions in contemporary economic and social contexts. 

This course introduces modern methods of supervised machine learning. It will consider data analysis models that can substantially improve prediction accuracy and interpretability compared with simple linear models, especially when faced with large datasets. Specifically, it will cover selection and shrinkage models such as Lasso, Ridge, Principal Component Analysis and Factor Models. General principles of model assessment and selection will also be discussed. The course is centred around concrete modelling applications using economic and financial datasets in Python, as you will learn valuable skills of applied data science and machine learning.