MS8956 - Advanced Regression Techniques | ||||||||||
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| * The offering term is subject to change without prior notice | ||||||||||
Course Aims | ||||||||||
This advanced Ph.D. level course delves into the
essential principles and methodologies of regression models, catering to
students pursuing a doctorate in the field. The curriculum encompasses a
comprehensive examination of several core topics, with a focus on fostering a
deep understanding of statistical techniques and their practical applications.
The course begins with an in-depth exploration of OLS (Ordinary Least Squares)
regression, covering finite sample properties, the Gauss-Markov theorem,
hypothesis testing, generalized least squares, and large sample theory.
Subsequently, students will gain expertise in panel data models, instrumental
variable techniques, and the General Method of Moments (GMM) estimation. The
curriculum also encompasses Bayesian Inference, facilitating a nuanced
understanding of probabilistic modeling and decision-making. As a tentative
component, the course may include the study of linear or nonlinear time series
models and other selected topics, depending on the interests of the students
and the instructor. Overall, this rigorous course aims to equip future scholars
with the essential statistical tools needed to excel in the field of Business
Analytics. | ||||||||||
Assessment (Indicative only, please check the detailed course information) | ||||||||||
Continuous Assessment: 40% | ||||||||||
Examination: 60% | ||||||||||
Detailed Course Information | ||||||||||
| MS8956.pdf | ||||||||||
