Economics 140 – Fall 2022
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Economics 140 – Fall 2022
Course Syllabus
Welcome to Economics 140! This course is meant to introduce you to the statistical analysis of economic data, also known as “Econometrics.” You should have taken both microeco- nomics and macroeconomics (either the Economics 100A/B or 101A/B series, or equivalent). More importantly, you have completed an introductory statistics course, e.g., Statistics 2, 20, 21, or an equivalent. On occasion, when it helps to explain a concept, we will make use of calculus – which is not a pre-requisite for the course, but it is a requirement for the Economics major. Those looking for a more mathematical and theoretical treatment of the same topics covered in this course are encouraged to take Economics 141.
After reviewing essential concepts from probability and statistics, we turn to the heart of the course: regression analysis. You will learn not only the meaning and properties of both univariate and multivariate regression, but also how to test economic relationships using real datasets and an econometrics software package. We will develop techniques to handle common statistical problems that arise when working with economic data including omiitted variable bias, measurement error, and simultaneous causality. We will expand the types of data we can analyze by exploring the topics of instrumental variables, quasi-experiements, and regression discontinuity.
General Information
Instructor: Stephen Bianchi, 673 Evans
Class Meeting Times: TTh 8:10-9:30am, Birge 50
Office Hours: T 2-3:30pm (and by appointment), 673 Evans
Enrollment: Please see the Economics Department Head GSI, Elena Ojeda (548 Evans, [email protected]), for ALL questions regarding enrollment.
Email: [email protected]
Email Policy: When you email me, please put “[ECON140]” in the subject and ask me questions that can be answered in a few sentences. If I find that my response will require more than a few sentences, I will ask you to come see me during office hours. I will reply to course related emails within 24 hours.
GSIs:
— Martin Caruso Bloeck (martin [email protected])
— Victoria Hollingshead ([email protected])
Discussion Sections: As in more recent semesters, you need NOT attend your first section meeting in order to remain enrolled in the course. Each GSI is only responsible for students who are officially registered in one of their sections, so please do not email another GSI. However, you may go to any GSI’s office hours. If you have a conflict, you may also attend your GSI’s other regularly scheduled section – but before doing so, please discuss with your GSI.
Accomodations: If you need disability-related accommodations in this class, if you have emergency medical information you wish to share with us, or if you need special arrangements in case the building must be evacuated, please inform John Wieselth- ier immediately. For disability-related accommodations, you must also obtain a Let- ter of Accommodation (LOA) from the Disabled Students’ Program (http://dsp. berkeley.edu), which they send electronically to me. Request for exam accommoda- tion must be received and acknowledged by me or Alexey at least two weeks before an exam, which is DSP’s own internal deadline for scheduling the proctoring of exams. Accommodations are not offered retroactively.
Academic Honesty: In fairness to students who put in an honest effort, cheaters will be harshly treated. Any evidence of cheating will result in a score of zero on that assignment. Cheating on the midterm or the final exam results in an “F” for the course. Cheating includes but is not limited to bringing unauthorized written or electronic materials into an exam, using unauthorized written or electronic ma- terials during an exam, copying off another person’s exam or assignment, allowing someone to copy off of your exam or assignment, having someone take an exam or assignment for you, changing an exam answer after an exam is graded, and plagia- rizing written or other materials. Incidences of cheating are reported to the Cen- ter for Student Conduct, which administers additional punishment. See also http: //sa.berkeley.edu/conduct/students/standards.
Limits to Confidentiality: As UC employees, all course instructors and tutors are Responsible Employees, and we are required to report incidents of sexual violence, sexual harassment or other conduct prohibited by university policy to the Title IX officer. We cannot keep reports of sexual harassment or sexual violence confiden- tial, but the Title IX officer will consider requests for confidentiality. There are confidential resources available to you, including the CARE Advocate Office (http: //sa.berkeley.edu/dean/confidential-care-advocate), which serves survivors of sexual violence and sexual harassment.
Honor Code: We at UC Berkeley have adopted this Honor Code: “As a member of the UC Berkeley community, I act with honesty, integrity, and respect for others.” Your
Econ 140 instructors join you in pledging to adhere to this code.
Course Books
Optional (but strongly recommended):
(MM) Joshua D. Angrist and J¨orn-Steffen Pischke, Mastering ’Metrics: The Path From Cause to Effect, 1st Edition.
(SW) James H. Stock and Mark W. Watson, Introduction to Econometrics, 3rd or 4th Edition.
There is a companion website for the 4th edition of Stock & Watson at: https://www. princeton.edu/~mwatson/Stock-Watson_4E/Stock-Watson-Resources-4e.html. Many study resources are available on this site including answers to end-of-chapter questions, datasets for empirical exercises, replication files for empirical analyses reported in the text- book, and additional empirical exercises. There is also a companion website for the 3rd Edi- tion at: https://wps.pearsoned.com/aw_stock_ie_3/178/45691/11696965.cw/index. html.
Course Software
The assignments in this course will be in Jupyter Notebooks using Python. Python is a general purpose open-source programming language utilized commonly by economists, data scientists, and programmers alike. We will primarily use the statsmodels library to carry out econometric analyses, in addition to pandas for data manipulation. All assignments will be distributed and completed in Jupyter Notebooks, an intuitive and interactive computing environment that contains both text and code.
Your notebooks will be hosted on DataHub, a free campus-wide cloud service that will provide the computing environment for your code. This means that you don’t have to install anything on your computer; instead you can access all assignments through a browser (preferably Google Chrome). If you have taken Data 8 or other data science courses on campus, the format should be familiar to you.
Do not worry if you have never used Python before; the first (optional, but highly rec- ommended) assignment in the class will help familiarize you with Python and the Jupyter environment. There are many other full-service econometrics packages (e.g., Stata, R, Mat- lab, SAS) but these will not be supported by your GSIs. Experience with Python can be helpful if you do other economic research (e.g., an honors thesis) and it looks good on your job resume.
Requirements
The course requirements include a midterm exam, a final exam, and five graded problem sets. The course grade will be determined as follows:
Problem Sets (40%)
Midterm (20%)
Final (40%)
Problem sets: You are encouraged (but not required) to form study groups of up to three students. The group may submit a single answer sheet with the names of all of the study group members at the top of the first page. Everyone in the study group receives the same grade. We will use the usual 3-point “check” system of grading problem sets. Answers to problem sets must be submitted via Gradescope by the specified time on the due date. No late work will be graded and, yes, that penalizes all members of the study group. Problem set 1 will be posted after the first lecture and is due Tuesday, September 13th.
Exams: There will be a midterm exam on Thursday, October 20th and a final exam on Monday, December 12th. If you do relatively better on the final than on the midterm, your final score will count for 60% of your overall class score. Dates for exams will not change and make-up exams will not be given. If you fail to take the midterm (for any reason), your final exam will count for 60% of your overall class score. If you fail to take the final (again, for any reason), you must petition for an incomplete. But please note that incompletes will not be granted unless you meet the University standards and those have become increasingly demanding. If you do not take the final and do not petition for an incomplete you will receive an “NP” or an “F” for the course (depending on your grading option).
Course Outline
The following is a tentative schedule of the topics to be covered in this class – it is likely to change a fair amount as we progress. The corresponding readings are from Angrist & Pischke (MM) and Stock & Watson, 4th Edition (SW). Lectures will loosely follow these readings.
● Classical Statistics & Simple Regression
– Lecture 1 (August 25): introduction, data types, random variables Readings: (MM) Introduction, Chapter 1; (SW) Chapter 1, Sections 2.1, 2.2
– Lecture 2 (August 30): random variables, probability distributions, random sampling and sample average
Readings: (MM) Chapter 1; (SW) Sections 2.3, 2.4 (pp. 33-35), 2.5, Appendix 2.1
– Lecture 3 (September 1): convergence of random variables, law of large num-
bers, central limit theorem, hypothesis tests
Readings: (MM) Chapter 1; (SW) Sections 2.6, 3.1
– Lecture 4 (September 6): t-tests, p-values, confidence intervals, testing for difference in means, multiple random variables, joint probability distributions, conditional probability
Readings: (MM) Chapter 1; (SW) Section 3.2, 3.3, 3.4, 3.7, Appendix 3.2
– Lecture 5 (September 8): interpreting statistical evidence, conditional expec- tation, law of iterated expectations (LIE), economic relationships and the condi- tional expectation function (CEF)
Readings: (MM) Chapter 2 (Appendix); (SW) Section 3.5
– Lecture 6 (September 13): CEF decomposition property, CEF prediction property, bivariate linear regression and the CEF, bivariate linear regression (es- timation)
Readings: (MM) Chapter 2 (Appendix); (SW) Section 4.1, 4.2
– Lecture 7 (September 15): linear regression model, unbiasedness and asymp- totic normality of OLSEs, goodness of fit
Readings: (MM) Chapter 2; (SW) Section 4.3, 4.4, 4.5
– Lecture 8 (September 20): goodness of fit, regression with binary independent variable and relation to difference in means testing
Readings: (MM) Chapter 2; (SW) Sections 5.1, 5.2, 5.3
– Lecture 9 (September 22): regression with binary independent variable, one- sided hypothesis tests, heteroskedasticity and homoskedasticity, nonlineariteis and simple linear regression
Readings: (MM) Chapter 2; (SW) Section 5.4
Multivariate Regression
– Lecture 10 (September 27): Gauss-Markov Theorem, multiple linear regres- sion, Frisch-Waugh Theorem
Readings: (MM) Chapter 2; (SW) Sections 5.4, 6,1, 6.2, Appendix 6.3
– Lecture 11 (September 29): comparisons of univariate and multivariate coef- ficent estimates, multicollinearity
Readings: (MM) Chapter 2; (SW) Sections 5.4, 6,1, 6.2
– Lecture 12 (October 4): irrelevant variables, omitted variables, goodness of fit in multiple linear regression
Readings: (MM) Chapter 2; (SW) Sections 6.3, 6.4, 6.5, 6.6, 6.7
– Lecture 13 (October 6): multivariate regression: Gauss-Markov Theorem, hypothesis testing
Readings: (MM) Chapter 2; (SW) Sections 7.1, 7.2, 7.3
– Lecture 14 (October 11): multivariate regression: hypothesis testing Readings: (MM) Chapter 2; (SW) Sections 7.1, 7.2, 7.3
– Lecture 15 (October 13): specifying a multivariate regression model, multi- variate regression: incorporating nonlinearities
Readings: (MM) Chapter 2; (SW) Section 7.5, 8.1, 8.2
– Midterm Review (October 18): catch up and review
– Midterm (October 20): 8-9:30am (Pacific Time), no lecture
– Lecture 16 (October 25): multivariate regression: single and mutliple binary independent variables, interaction terms
Readings: (MM) Chapter 2; (SW) Sections 8.2, 8.3
– Lecture 17 (October 27): internal and external validity Readings: (MM) Chapter 2; (SW) Sections 9.1, 9.2
IV, Differences-in-Differences, Regression Discontinuity
– Lecture 18 (November 1): instrumental variables, indirect least squares (ILS) Readings: (MM) Chapter 3, (SW) Section 12.1
– Lecture 19 (November 3): instrumental variables, two stage least squares Readings: (MM) Chapter 3, (SW) Section 12.2
– Lecture 20 (November 8): testing overidentifying restrictions Readings: (MM) Chapter 3, (SW) Section 12.3
– Lecture 21 (November 10): efficiency of OLS estimators versus IV estimators, testing for endogeneity
Readings: (MM) Chapter 3, (SW) Section 12.3
– Lecture 22 (November 15): instrumental variables, emprical example Readings: (MM) Chapter 3, (SW) Section 12.4
– Lecture 23 (November 17): differences-in-differences estimator Readings: (MM) Chapter 5, (SW) Sections 13.4
– Lecture 24 (November 22): regression discontinuity (RD), sharp RD Readings: (MM) Chapter 4, (SW) Sections 13.4
– Lecture 25 (November 29): regression discontinuity (RD), fuzzy RD Readings: (MM) Chapter 4, (SW) Sections 13.4
– Final Review (December 1): catch up and review
Final Exam (December 12): 3-6pm (Pacific Time)
2022-09-13