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Economics 140 – Spring 2022

Course Syllabus

2022


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, W21, 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 endogeneity and selection bias. We will expand the types of data we can analyze by exploring the topic of quasi-experiments.


General Information

• Instructor: Stephen Bianchi, 673 Evans

• Class Meeting Times: MWF 4:10-5pm, Dwinelle 155

• Office Hours: Tu 4:15-5:45pm, 673 Evans

• Enrollment: Please see the Economics Department Head GSI, John Wieselthier (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 fifind 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.

• Lead GSI: Gabriel Granato ([email protected])

• Discussion Sections: Due to remote instruction, you need NOT attend your first section meeting in order to remain enrolled in the course. This is a change from past semesters with on-campus instruction. 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 materials during an exam, collaborating with another person during an exam, having someone take an exam or assignment for you, changing an exam answer after an exam is graded, and plagiarizing written or other materials. Incidences of cheating are reported to the Center 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), it is widely used in private industry, and it looks good on your resume (CV).


Requirements

The course requirements include one midterm, a final, and five graded problem sets. The

course grade will be determined as follows:

• Problem Sets (40%)

• Midterm (25%)

• Final (35%)

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 Friday, February 11th.

Exams: There will be a midterm exam on Wednesday, March 16th and a final exam on Wednesday, May 11th. If you do relatively better (i.e., earn a higher standardized score) 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 have not petitioned for an incomplete (prior to the final exam time) 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. Topics with (*) beside them are tentative and will be covered if time permits.

• Classical Statistics & Simple Regression

– Week 1 (01/18 - 01/21): introduction, selection bias, data types, random variables

Readings: (MM) Introduction, Chapter 1; (SW) Chapter 1, Sections 2.1, 2.2

– Week 2 (01/24 - 01/28): random variables, probability distributions, random sampling, sample average, sample variance, hypothesis tests

Readings: (MM) Chapter 1; (SW) Sections 2.2, 2.4 (pp. 33-35), 2.5, Appendix 2.1

– Week 3 (01/31 - 02/04): t-tests, p-values, confidence intervals, interpreting statistical evidence, testing for difference in means, multiple random variables, joint probability distributions, conditional probability, law of iterated expecta-tions (LIE)

Readings: (MM) Chapter 1; (SW) Sections 2.3, 3.1 - 3.4, 3.7, 5.1, 5.2

– Week 4 (02/07 - 02/11): convergence of random variables, law of large num-bers, central limit theorem, conditional expectation, economic relationships and the conditional expectation function (CEF), CEF decomposition property, CEF prediction property

Readings: (MM) Chapter 1; (SW) Sections 2.6, Appendices 3.2, 3.3

– Week 5 (02/14 - 02/18): simple linear regression and the CEF, simple linear regression (estimation), properties of ordinary least squares estimators (OLSEs), ordinary least squares (OLS) assumptions

Readings: (MM) Chapter 2 (Appendix); (SW) Sections 4.1, 4.2, 4.4, 4.5

– Week 6 (02/22 - 02/25): regression and causality, unbiasedness and asymptotic normality of OLSEs

Readings: (MM) Chapter 2 (Appendix); (SW) Appendices 4.2, 4.3

– Week 7 (02/28 - 03/04): regression useful facts, goodness of fit, regression R2, regression with binary independent variable and relation to difference in means testing, heteroskedasticity and homoskedasticity

Readings: (MM) Chapter 2; (SW) Section 5.3, 5.46

• Multivariate Regression

– Week 8 (03/07 - 03/11): multiple linear regression, Frisch-Waugh Theorem, simple linear regression vs multiple linear regression, OLS assumptions and prop-erties of OLSEs, multicollinearity, goodness of fit, R2 and adjusted R2 , omitted variable bias (OVB)

Readings: (MM) Chapter 2; (SW) Sections 6.1 - 6.7, Appendices 6.1, 6.3

– Week 9 (03/14 - 03/16): midterm review and midterm (no lecture 03/16)

– Midterm Exam (03/16): 7-9pm (Pacific Time)

– Week 10 (03/28 - 04/01): joint and linear hypothesis testing, F-statistic, F-test, conditional mean independence

Readings: (MM) Chapter 2; (SW) Sections 7.1, 7.2, 7.3, 7.5

– Week 11 (04/04 - 04/08): multivariate regression: incorporating nonlinearities, interaction terms, external and internal validity, simultaneous causality

Readings: (MM) Chapter 2; (SW) Sections 8.1, 8.2, 8.3, 9.1, 9.2

• IV, Differences-in-Differences, Regression Discontinuity

– Week 12 (04/11 - 04/15): instrumental variables (IV), indirect least squares (ILS), two stage least squares (TSLS)

Readings: (MM) Chapter 3; (SW) Sections 12.1, 12.2

– Week 13 (04/18 - 04/22): testing overidentifying restrictions, efficiency of OLS estimators versus IV estimators

Readings: (MM) Chapter 3; (SW) Sections 12.3, 12.5

– Week 14 (04/25 - 04/29): testing for endogeneity (IV), differences-in-differences estimator, regression discontinuity (*)

Readings: (MM) Chapters 4, 5; (SW) Sections 12.4, 13.1, 13.4

– Week 15 (05/02): final review

• Final Exam (May 11): 8-11am (Pacific Time)