BU.232.620.W3 – Linear Econometrics for Finance

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Linear Econometrics for Finance

2 Credits

BU. 232.620.W3

Tuesdays, 8:30AM-11:30AM

October 31, 2023-December 19, 2023

Fall II, 2023

Washington D.C (555 Penn)

Instructor

Gianni De Nicolò

Contact Information

[email protected]

Office Hours

After class or by Zoom scheduled appointment

Teaching Assistant

Minzheng Wu

Email: [email protected] 

Texts & Learning Materials

· Lecture Notes (LN) will be posted on Canvas before class

· Textbook:

Ø Introductory Econometrics: A Modern Approach, 7th Edition, by Wooldridge, from Cengage. The eBook is available for purchase through Cengage Unlimited (ISBN 9780357700037).   

Course Description

Linear Econometrics deals with the estimation of linear economic models. This is a quantitative class requiring strong foundations in multivariate calculus, matrix 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, the multivariate linear regression model, regression functional forms, conditional heteroskedasticity, and stationary and nonstationary time series models.

Prerequisite(s)

BU.510.601

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 and time series data structures.

Attendance

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.

Assignments & Rubrics

Students will deliver Homework problem sets individually.

Students will be assigned to Groups at the beginning of the course week

Group projects. Groups will deliver Group Projects GP. Projects will include a set of computer exercises. Details on GP assignments will be provided on Canvas and discussed in class.  

Grading

The grade of A is reserved for those who demonstrate extraordinary performance as determined by the instructor. The grade of A- is awarded only for excellent performance. The grades of B+ and B are awarded for good performance. The grades of B-, 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. The grade of F indicates the student’s failure to satisfactorily complete the course work. For Core/Foundation courses, the grade point average of the class should not exceed 3.35. For Elective courses, the grade point average should not exceed 3.45.

Tentative Course Calendar
The instructor reserves the right to alter course content and/or adjust the pace to accommodate class progress. Students are responsible for keeping up with all adjustments to the course calendar.