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Linear Econometrics for Finance
BU.232.620.
Texts & Learning Materials
· Lecture Notes (LN) will be posted in Canvas
· Textbook (recommended)
Ø Basic: “Introduction to Econometrics” (4th Edition) by James Stock and Mark Watson (SW).
Ø Advanced: “Foundations of Modern Econometrics: A Unified Approach”, by Yongmiao Hong
Ø Math Prerequisite
o “Probability and Statistics for Economists”, by Bruce Hansen.
o Appendix A of “Econometrics”, by Bruce Hansen
· Programing:
You need a programing package implement econometric analyses. Python – as discussed in Computational Finance (BU232.610) – can be used, while others like Stata, R, and MATLAB are also fine. Our course teaches Econometrics, not coding; learning to code is your responsibility!
Course Description
Linear Econometrics deals with the estimation of linear econometric models. This is a quantitative class requiring strong foundations in multivariate calculus, linear algebra, probability and statistics as pre-requisites. The course covers linear regression models with both finite-sample and large-sample inference. Topics include the univariate linear regression model, multivariate linear regression model, regression functional form, conditional heteroskedasticity, and weighted least squares, as well as advanced topics such as instrumental variables, time series models and linear panel regression models, if times permits. Focus is placed on understanding fundamental concepts and developing the skills necessary for robust application of regression techniques. A significant amount of time will be spent on empirical applications. Given the quantitative nature of the course, we shall combine the in-class lecture (2 hours) and zoom TA session (1-hour) in each week.
Prerequisite(s)
BU.510.601, Multivariate Calculus, Linear Algebra, Advanced Probability and Statistics
Statement about Finance and Social Responsibility
The effectiveness and perceived integrity of finance have been tested in recent years. Along with preventable excesses and regrettable distortions, financial innovation has, however, always been an effective means for society to achieve its goals, from insurance to consumption to saving. The power of financial innovation as a generator of inclusive prosperity and widespread well-being can (and should be) reclaimed. In this context, optimization of shareholder’s value – for instance – may not be the only metric along which financial success is measured and should be placed, along with other traditional finance metrics, in the broader context of its contribution to society. To this extent, Carey encourages technical, non-ideological, exchanges of ideas leading to a better understanding of the broader role of finance as a force for shared prosperity. The first class provides an initial opportunity for technical discussions of these issues as they relate to the topics covered in Financial Econometrics.
Learning Objectives
By the end of this course, students will be able to:
1. Understand the basic theory of econometric estimation.
2. Compute ordinary least squares (OLS) estimator.
3. Conduct hypothesis testing and model evaluation of linear regression models with cross section, time series and panel data structures.
To view the complete list of Carey Business School’s general learning goals and objectives, visit the Carey website.
Assignments (TBD)
|
Assignment |
Learning Objectives |
Weight |
|
Homework Assignments (three) |
1–3 |
15% |
|
Empirical Projects (three) |
1–3 |
50% |
|
Final Exam (take-home) |
1–3 |
25% |
|
Class Participation and Attendance |
1–3 |
10% |
|
Total |
|
100% |
Homework Assignments (15%) (TBD)
Individual
Empirical Projects (50%) (TBD)
Individual; submit an analysis report for each; not graded for project 1, 15% for project 2, and 35% for project 3.
Final Exam (25%) (TBD)
Individual; take-home; open-book
Class Participation and Attendance (10%) (TBD)
Students are expected to attend all scheduled class sessions. Failure to attend class will result in an inability to achieve the objectives of the course. Full attendance and active participation are required for you to succeed in this course.
Class participation is a measure of engagement in class. It is not just a measure of the quality and quantity of your exchanges with the instructor, but also with your peers in the class. Not coming to classes, failing to take part and/or not engaging with other students in class discussions, etc. will earn no points in class participation.
Important note: No make-up of Empirical Projects and Final Exam for any reasons of missing the class; late submissions will NOT be accepted under any circumstances. Moreover, no Remote replacement for any in-person class.
Grading
The grade of A is reserved for those who demonstrate extraordinarily excellent performance as determined by the instructor. The grade of A- is awarded only for excellent performance. The grades of B+, B, and B- are awarded for good performance. The grades of C+, C, and C- are awarded for adequate but substandard performance. The grades of D+, D, and D- are not awarded at the graduate level (undergraduate only). The grade of F indicates the student’s failure to satisfactorily complete the course work.
Please note that for Foundation and Core courses, a maximum of 25% of students may be awarded an A or A-; the grade point average of the class should not exceed 3.3. For Elective courses, a maximum of 35% of students may be awarded an A or A-; the grade point average of the class should not exceed 3.4. (For classes with 15 students or fewer, the class GPA cap is waived.)