Econometric Theory and Application
Recap on statistics before taking a deep dive into linear regression analysis – a skill that allows professionals to forecast future trends. Cover simultaneous equation models, selected time-series data analysis methods and more.
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- 30 Nov 2020
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At the completion of the subject students will be able to:
- explain the theoretical foundation behind linear regression analysis
- examine the assumptions and properties of various estimators, including least squares estimators and Maximum Likelihood estimator
- formulate economic/financial hypotheses into statistically testable forms and conduct these tests using real-life economic/financial data
- evaluate the adequacy of various statistical models/tests as they are applied to real life data
- identify and examine various applied topics in econometrics.
- Statistics and Probability Review
- Classical Linear Regression and Least Squares (LS) Estimation
- Multivariate Linear Regression Model
- Properties of LS Estimator; Statistical Inferences and Hypothesis Testing
- Heteroskedasticity, Autocorrelation
- Diagnostic Testing
- Simultaneous equation models,
- Time-series analysis
- Volatility modelling
- Discrete-choice models
In order to enrol in this subject, you must be accepted into one of the following degrees:
No special requirements
The subject starts with a brief review of statistics and probability theories. It then discusses the theory of linear regression analysis.The second part of the subject will be devoted to applied topics in econometrics, including: simultaneous equation models, qualitative and limited dependent variable models, and selected time-series data analysis methods. The practical uses of the techniques introduced in the subject are demonstrated through their applications to the analysis of economic/financial data in laboratory classes.
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- Project (50%)
- Case study (50%)
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