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PADM-GP 2902: MULTIPLE REGRESSION AND INTRODUCTION TO ECONMETRICS
创建日期：2024-05-27 14:51:10
所属分类：经济Economics
标签： PADM-GP 2902
MULTIPLE REGRESSION AND INTRODUCTION TO ECONMETRICS

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PADM-GP 2902: MULTIPLE REGRESSION AND INTRODUCTION TO ECONMETRICS

(every other week)
Adelaide Currin
Email: [email protected]
Recitation: Tuesdays 8:35-9:35PM
Office Hours: Fridays 1PM-2PM
(every other week)
TC-led recitations and office hours are virtual.
COURSE DESCRIPTION
Multiple regression is the core econometric technique used by policy and financial analysts. In
this course, you learn how to use and interpret this critical statistical method. Specifically, you
learn how to build and estimate multiple regression models, how to evaluate whether regression
coefficients are biased, whether standard errors (and thus t statistics) are valid, and whether
regressions used in policy and finance studies support causal arguments.
In addition, employing one consistent dataset for all your computer exercises, you perform
statistical analyses discussed in class using Stata, an econometric statistical package, and you see
how the results reflect econometric concepts. Finally, with a group of your classmates and project
datasets provided by your professor, you do a project that involves estimating your own
regression model and applying the techniques we learn in class.
PREREQUISITE: CORE-GP 1011 or equivalent
COURSE LEARNING OBJECTIVES
• Understand what an Ordinary Least Squares (OLS) regression does and why it is useful.
• Write and interpret mathematical equations representing various regression models.
• Interpret regression results as they are typically represented in statistical software
packages, policy and finance papers, and academic articles.
Page 2 of 6
• Use Stata and regression techniques to suggest answers to important policy questions.
• Think critically about the assumptions underlying your (or another analyst’s)
interpretation of regression output and test whether these assumptions hold.
• Conduct a research project in which you formulate, estimate, write about, and present an
econometric model.
• Understand the statistics that underlie research in your field of interest.
RECITATIONS (Virtual):
Attendance is optional by highly recommended. In these sessions you:
• review the answers to the problem set and computer exercises due the previous week
• discuss the learning objectives of the problem set and computer exercises due the
following class
The TCs also offer one hour of office hours per week (see course schedule).
There are no problem sets or computer exercises due in the first week of class, so the first week
of classes there will be a STATA tutorial/refresher during the recitation on Monday January 23
(12:30-1:30PM) and during TC office hours on Friday January 27 (1:00-2:00PM).
COURSE MATERIALS
REQUIRED: A.H. Studenmund, Using Econometrics: A Practical Guide. ISBN: 9780134182742
• Unfortunately, the text is not available electronically from NYU Libraries (though you can
ask them to scan a limited number of pages/chapters for you).
REQUIRED: STATA/BE. You can purchase this and install it on your personal computer or use it
from NYU’s virtual lab.
• Purchase: Use this link to obtain a student discount. The least expensive option is a 6-
month license. If you are planning to take Estimating Impact, Advanced Empirical
Methods, or the Research Capstone you may want to consider an annual or perpetual
license.
REQUIRED: NYU BRIGHTSPACE. All announcements and class-related documents (lecture power
points, datasets for the final project, problem sets, computer exercises, assignment solutions,
STATA review materials, and exam review materials) are available on Brightspace. Problem sets
and computer exercises are to be submitted via Assignments in Brightspace.
Page 3 of 6
COURSE REQUIREMENTS AND GRADING
• 20% Problem Sets and Computer Exercises
• 35% Midterm Exam
• 45% Regression Project
Students are expected to attend all of the lectures and actively participate. Please email your
instructor if you are unable to attend class due to extenuating circumstances.
PROBLEM SETS AND COMPUTER EXERCISES (20%)
There are 15 total problem sets (PS) and computer exercises (CE), named for the class in which
they’re due. We will drop the lowest two from your grade. Due to this flexibility we will not accept
late assignments; please contact me if you have extenuating circumstances. You must complete
PS 9 and CE 9; these cannot be dropped from your grade.
Problem sets/computer exercises are graded for completion, not correctness. Students should
take these assignments seriously as they’re good preparation for the exam and final project.
For the STATA code and output for computer exercises, submit the log file with the last “run” of
the analysis as a PDF. Please submit written answers to computer exercises and problems sets as
a Word file.
MIDTERM EXAM (35%)
An exam will be given during Class 11 on April 20th (see course schedule on the following page).
You may use a non-graphing calculator and two pages (single-sided) of notes.
GROUP PROJECT (45%)
In groups of 4-5 you will conduct a regression analysis, present your results, and write a paper.
Note all group members will complete peer evaluations that will factor into grades.
• Fill out Project Data Preference Form by EOD Thursday, February 2nd.
• Read Chapter 11, “Running Your Own Regression Project”, in the course text.
• Once groups have been assigned, meet with your group as soon as possible to plan the paper.
Teams must email me the question you propose to answer and at least one specification that
will be estimated by Thursday, March 2nd.
• Contact your instructor to meet with your group during the week of March 20th to discuss the
project. You should have descriptive statistics and initial results.
• Present your results during class April 27th or May 4th to get feedback.
• Write an 8–10-page paper, including two tables, organized as follows (tables do not count in
the page limit). DUE THURSDAY MAY 11. We also ask students to submit a group evaluation
form, to help ensure that all group members participate equally in the final project.

Page 4 of 6
FINAL PROJECT OUTLINE
1. Introduction: What is the goal of your regression study? Why is it interesting? Why do
we care? (This does not have to be momentous, but you should explain why the
results could be interesting or valuable.)
2. Data: Describe your sources and discuss the descriptive statistics in Table 1.
3. Model and Empirical Strategy: What is your model (equation) and how does it achieve
the goal of your analysis? Why are the specific variables used and measured as they
are? Do you have any prior expectations about the signs of coefficients? How will you
estimate the model? (Usually OLS with fixed effects.)
4. Results: Discuss the Results presented in Table 2.
5. Conclusions: What does your model say about your goal or issue? What is the next
step in this research?
6. Appendix:
i. Table 1 (with good, descriptive title): Descriptive statistics of all the variables
in your model(s).
ii. Table 2 (with good descriptive title): Results of your models, presented in 4-5
columns.
iii. Final annotated Stata log file of your results (note this should be “clean”, i.e.
contain no errors).

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