DESCRIPTION | This course introduces students to economics. It consists of three modules: basics of ecomomics (Module1), microeconomics (Module 2) and macroeconomics (Module 3). Module1 covers the basic principles of economics, supply, demand, market equilibrium, welfare, market failure, and the rationale for government intervention. Module 2 covers the optimal decision of consumers and firms, market power and different market structures, Labor markets and inequality, etc. Module 3 includes measuring aggregate economic activities, consumption and investment, money and financial intermediaries, aggregate demand, aggregate supply and business cycles, macroeconomic stability policy, and economic growth. |
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Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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L03 (6546) | Tu 06:00PM - 08:50PM | Rm 233, W1 | YUEN, Cheuk Yi Kelvin | 35 | 24 | 11 | 0 | |
T03 (6549) | Fr 04:30PM - 05:20PM | Rm 233, W1 | YUEN, Cheuk Yi Kelvin | 35 | 24 | 11 | 0 |
VECTOR | [3-0-0:3] |
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DESCRIPTION | The course aims to provide a comprehensive understanding of the city and the system of cities, the challenges faced by cities, especially the rapidly-developing large cities, and the key tools for interventions in response to critical pressures linked to economic development, urbanization, globalization, migration, social inclusion, climate change, resource efficiency, technology etc. |
Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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L01 (6254) | Mo 09:00AM - 11:50AM | Rm 202, W4 | CAO, Rui KAN, Ge Lin LIU, Xiaofeng YUEN, Cheuk Yi Kelvin | 30 | 24 | 6 | 0 |
VECTOR | [3-0-0:3] |
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DESCRIPTION | This course builds on the knowledge of the linear regression models to introduce students advanced statistical methods to analyze survey, administrative and other types of data of interest to quantitative social scientists. The introduction of statistical methods is integrated into research contexts and designs from a holistic framework and bridge quantitative social science and computational social science (data science). Topics include measurement, prediction, causal inference, natural experiment and program evaluation (difference-in-differences, panel data, instrumental variables, regression discontinuity), applied to both survey and big data. |
Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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L01 (6255) | Tu 12:00PM - 02:50PM | Rm 205, C7 Library | YUEN, Cheuk Yi Kelvin | 30 | 14 | 16 | 0 |