Syllabus-3

Brooklyn College - Department of Economics
ECON 4400W: Advanced Economics and Business Statistics

Spring 2025 Faculty: Khaled Eltokhy (keltokhy@gradcenter.cuny.edu) Class Website: Blackboard Office Hours: Wednesday 12:00 PM - 2:00 PM (via Zoom, email ahead for link) Final Exam: None Meeting Times:

  • Monday 2:15 PM - 3:30 PM in Whitehead 208
  • Wednesday 2:15 PM - 3:30 PM on Zoom (link on Blackboard)

1 Course Description

This course emphasizes the practical use of basic econometric techniques, which are essential tools for estimating and testing economic relationships. These methods are applicable in various business disciplines such as accounting, finance, marketing, and management, as well as in social sciences.

Upon successful completion, students should be comfortable with basic statistics and probability, capable of using statistical software to estimate econometric models, and proficient in reporting results in a non-technical and literate manner. The focus is on estimating and interpreting linear regression models.

Key Topics Covered

  • Univariate linear regression: least squares, coefficient of determination, model assumptions, significance tests, ANOVA, confidence and prediction intervals, scatter plots, residual analysis, and outlier detection.
  • Multiple linear regression: least squares, coefficient of determination, model assumptions, significance tests, confidence intervals, and residual analysis.
  • Regression model building: general linear model, variable selection, dummy variables.
  • Assumption violations: multicollinearity, heteroscedasticity, and autocorrelation.
Prerequisites: Econ 3400 or Math 3501 (C- or higher) and Econ 3410 (C- or higher); or Math 1231 or Math 1201.

2 Textbook and Software

Primary Textbook:

  • Stock, J.H., \& Watson, M.W. (3rd Edition). Introduction to Econometrics.

Optional:

  • Wooldridge, J.M. Introductory Econometrics.
Software: Microsoft Excel (available through CUNY Office 365).

3 Grading

| Assessment | Weight | | :--- | :---: | | Homework (5 sets) | $15 \%$ | | Midterm 1 (March) | $20 \%$ | | Midterm 2 (May) | $30 \%$ | | Presentation | $10 \%$ | | Research Paper | $20 \%$ | | Attendance/Participation | $5 \%$ |

Final grades will be curved: $30 \%$ A's, $30 \%$ B's, $30 \%$ C's, $10 \%$ D/F's.

Homework Grading Scale

  • $\checkmark+$ (100 points): Significant effort, strong overall results.
  • $\checkmark$ ( 85 points): Substantial but incomplete progress.
  • $\checkmark$ - (65 points): Poorly developed problem set.
  • 0 points: Not submitted.
Late submissions and identical assignments will receive a zero.

4 Term Paper and Presentation

Term Paper (20\%) Due May 19:

  • Introduction: Motivation, economic model, empirical methodology.
  • Results: Statistical findings, regression analysis, economic implications.
  • Conclusion: Summary, comparison with literature, policy implications.
Submit via email in PDF format. Late papers incur penalties. Presentation (10\%):
  • 10-12 minutes in the last four classes.
  • Use PowerPoint or similar.
  • Attendance at others' presentations is graded.

5 Course Outline

  • Feb. 3: Introduction (Chapter 1)
  • Feb. 5 - Feb. 19: Review of Statistical Concepts (Ch. 2-3)
  • Feb. 23: Research Project Problem Statement Due
  • Feb. 24 - Mar. 17: Univariate Linear Regression (Ch. 4)
  • Mar. 19: Midterm Exam 1 (In Person)
  • Mar. 24 - Apr. 2: Inference in Univariate Models (Ch. 5)
  • Apr. 6: Research Project Model Description Due
  • Apr. 7 - Apr. 30: Multivariate Regression (Ch. 6-7)
  • May 5: Midterm Exam 2 (In Person)
  • May 7 - May 14: Research Project Presentations (Online)
  • May 19: Term Paper Due

POLICIES

1. Academic honesty is expected. (see https://www.brooklyn.cuny.edu/web/aboutinitiatives/ policies.php) You will receive a mark of zero on any work where cheating or plagiarism occurs. Students suspected of cheating will be reported. 2. Attendance is required and taken for the course grade. I will take 10 attendances randomly, which, along with class participation, account for $5 \%$ of the course grade. 3. Come to class on time; you will not be given extra time if you are late for an exam. 4. There will be no makeup examinations. Exam dates are given in the course outline below. This is to avoid any potential conflicts. 5. Keep cell phones shut off during class. During exams, you may not use your cell phone as a calculator. You may bring a calculator without programming capabilities. 6. Students must turn the assignments on time. Late assignments will not be accepted. 7. Any re-grading requests must be submitted to me and in writing within one week of the date the graded exams are returned. The entire exam will then be re-marked in light of the information you provide, which may result in an increase or decrease in your grade. 8. If you decide to withdraw from this class, make sure you do so with the registrar. If you withdraw without permission, you will be assigned a failing grade. 9. If you have a learning disability or a physical disability that requires accommodation, please let me know as soon as possible. All needs that have been verified through the Services to Students with Disabilities will be accommodated. 10. In the event that a student needs to be out of class, relevant materials will be shared on Blackboard. Please email me if you will be out of class. 11. In the event that the course needs to be offered entirely online for a particular class meeting, we will meet synchronously at the standard class time using Zoom. Additional instructions about the particular details of class meetings or schoolwork will be emailed to you in the event of a shift to online instruction.