GPCO454: Quantitative Methods II (QM2) Syllabus Prof. Francisco Garas Winter 2021 Course Information Room: East Warren Tent and remote Lectures: Monday/Wednesday/Friday 10:00–10:50am Labs: Wednesday 7:00–8:20pm (remote) ursday 2:00–3:20pm (remote) ursday 6:00–7:20pm (remote) Oce Room: RBC 1311 Email: fgar
[email protected] Overview and Course Goals How can we quantify relationships in the world around us? Whether studying public policy, management decisions, labor, international and domestic politics, development, or the environment, the basic quantitative methodology is oen the same: applied regression analysis. QM2 introduces this methodology, as well as the types of data oen encountered in policy research and the challenges that researchers face in analyzing such data.e focus of this course is cross-sectional regression analysis, emphasizing the principles of basic model-building, threats to validity, and proper interpretation of results. e course begins with simple (bi- variate) regression and moves to multiple regression, drawing on a wide array of examples. Students will be expected to understand the basic derivation and properties of the regression estimator in both scalar andma- trix notation, the assumptions behind the linear regression estimator, the implications of those assumptions for analyzing data, and the methods used to address violations of those assumptions.e goal of the course is to provide students with the statistical tools and basic programming skills necessary to independently address research questions of interest. Course Structure M/W/F sessions in total will consist of approximately 70% lecture/discussion and 30% hands-on practical applications of the material covered and its connections to labs, assignments, and current events.e TA-led sessions are a set of structured labs designed to help students learn how to translate theory into action. You are required to attend the labs (even if asynchronously). Lectures will assume that students have completed background readings prior to class each Monday. You will also need to watch any pre-recorded material that is made available prior to the beginning of the relevant lecture. Lab sessions will be conducted in smaller groups, led by TAs, and will focus on the building blocks of analysis, programming, interpretation, and presentation of results in Stata. Although there are other languages that can be used to similar end (R,Matlab, Python, etc.), we are using Stata because it is straightforward, powerful, and optimized for regression analysis. It is also the language used in many of the upper-level courses here at GPS.at being said, Stata is simply a tool to teach applied regression analysis, and it is my goal that you be able to apply the skills learned in this course using other languages should the need arise in your career. To support this latter goal, the lab material will be also oered in R for you to review on your own. Teaching Assistants • Marianna Garcia (
[email protected]) • Jesse Nasland (
[email protected]) • Songyue Zhang (
[email protected]) Prof. Francisco Garas Winter 2021 2 Oce Hours I will hold oce hours on Mondays from 1:00–2:30pm remotely. You are welcome to join in at your conveni- nence during this time. You do not need an appointment during oce hours.e TAs will also hold weekly oce hours, as detailed below. Sta Member Time Location Francisco Garas M 1:00–2:30PM Remote Marianna Garcia M 5:00–7:00 PM Remote Jesse Nasland Tu 5:30–6:30 PM and Friday 9:00–10:00am Remote Songyue Zhang Tu 12:30–1:30 PM andW 8:30–9:30 AM Remote Course Materials Textbooks Agreat supplement to the lectures is Introductory Econometrics: AModernApproach, by JereyM.Wooldridge (4th or 5th edition).e course will use the same notation as in the book. You are not obligated to purchase the textbook. Youmay be able to borrow a copy from a 2nd-year GPS student; copies are also on reserve at the library. If you do want to purchase the book, the bookstore is stocking the 5th edition. (For those interested, QM3 will be using Wooldridge as well.) I will also assign several other readings during the quarter that will be available electronically; in particular I will draw fromMastering Metrics:e Path from Cause to Eect, by Joshua Angrist and Jorn-Steen Pischke, and from Peter Kennedy’s A Guide to Econometrics. Soware You are required to have access to a copy of Stata (version 14 or newer). You may use free campus computing resources, available through CloudLabs; see detailed instructions here. If you opt to purchase a license on your own, please acquire Stata/IC (or higher). You must have Stata installed on your computer (or have access through CloudLabs) and know how to start Stata before the beginning of the rst class. It is your responsibility to make sure you are set up to access CloudLabs during lab sessions and lecures. You must have Stata installed on your computer, or have access to CloudLabs, and know how to start Stata before the beginning of the rst class. Class time should not be the rst time you have tried to open the program! If you do this you will be behind from day one, and you will carry the consequences into the quarter. Website All coursematerials, communications, discussions, and assignment submissions will take place via the course Canvas website. Because of the large size of the class, the course sta will not be able to respond to most individual emails in a timely manner. For this reason, questions related to course content, submission of assignments, logistics, etc. should be posted on the appropriate forum on Canvas. Please ensure that you have access before the start of the quarter; if you need help getting access because you are not a GPS student, please contact GPS student services. A Note All GPS courses are dicult; QM2 will challenge you in the breadth of skills required. You will be asked to master (a) the theoretical underpinnings of regression analysis, (b) the conceptual skills to build and interpret regression models, and (c) the programming and data management skills necessary to conduct your own analyses. is is a course that deeply rewards hard work, especially at the beginning of the quarter. e learning curve for much of the material covered is steep and the course consistently builds on early material. It is therefore a wise strategy to be on top of the workload early. Many students nd this course painful in the moment, but very rewarding later: our alumni consistently report that the skills learned in the QM sequence are among the most valuable they acquire at GPS. Prof. Francisco Garas Winter 2021 3 Virtual Etiquette As we have now partially switched to online forums, some of you might be unfamiliar with the etiquette of online behavior. In general, a good rule of thumb is to treat others online as youwould in real life, with respect. Professors, TAs, and assistants reserve the right to kick, ban, and report any member at any time if we nd you have violated the rules outlined below. Here are some general rules that may seem fairly obvious, but we are putting them here to be clear on what our expectations are of you as professionals. General Rules: • Do not be disrespectful towards anyone, TAs, assistants, professors, or classmates. • Do not spam TAs, assistants, or professors about anything. We try our best to be responsive, so we will get you as soon as we can. • No names or prole pictures that are NSFW (Not Suitable for Work) will be tolerated. • No streaming/showing NSFW content such as pictures or videos related to pornographic content, screamers, or any other website with inappropriate content. • No links to NSFW content such as anything related to phishing/scams, viruses, pornographic content, screamers, or any other website with inappropriate content. • Name calling, harassment, or threats are not tolerated. • No discriminatory language (ex: racist, homophobic) or hate speech will be tolerated. Prior to class: • Test yourmicrophone. You can change your settings to automatically prompt an audio test before class. • You should receive a notication that our class sessions are being recorded. Zoom Policies We will connect to Zoom using the class ID provided on Canvas. We will try to behave as much as possible as if we were in the physical classroom. Please follow these guidelines: • If you are in the continental United States or corresponding time zones, I expect you to be present in class in real time. If you are not on continental US time zones, I will be recording classes for you to watch. • To the extent possible, choose a quiet place to connect to the class. • Have your full name displayed as your prole name on Zoom. • Have your camera on for the duration of the class.is helps me read your facial expressions as I would in the classroom and adjust speed and content accordingly. • Your physical appearance should be appropriate for our professional environment. Also, choose a GPS virtual background: https://gps.ucsd.edu/about/branding.html#Backgrounds • Mute your microphone for the duration of the class, with the exceptions specied below. • To ask a question, you can either raise your hand-iconorwrite a question in the chat (note that questions can be public—for everybody to see—or directed only to the host). If you raise your hand-icon, the host will direct you to unmute your microphone. • You can use the “go slower” icon if you would like the host (me) to slow down. You can alternately use the chat for that purpose. • Have paper/pencil or electronic le to take notes, as you would do in a physical class. Prof. Francisco Garas Winter 2021 4 Class Participation • You must participate through video and audio only. • Please utilize the “raise hand” buttonwhen youwant to answer a question.is can be found by opening the “Participants” tab, which will also give you options such as yes, no, thumbs down, etc. • Please lower your hand and reset your reaction aer you have been called/the conversation has moved on, this prevents you from being double called accidentally. • When you are called, please unmute yourself. We will try to wait for a second to give you time to unmute. You can also connect the unmute button to a key, such as pressing the spacebar to talk. • You must not share your screen unless called upon to do so • Youmust not use the chat feature for private o topic discussions, only for contacting the TA/assistants for technical help. Be aware that everything you chat is automatically put on record, even private chats. Assignments and Grading Homework Assignments (40%) You will be assigned four homework assignments throughout the quarter. Each homework assignment will consist of two parts: a Stata .do le and an accompanying written assignment that you will submit electron- ically to Canvas before the start of class on the due date. Late submissions (as dictated by the server time stamp) will be considered late and will not be graded (i.e., you will receive a zero for the assignment). You may work alone or in groups for homework assignments; I want you to do what works best for your own learning. But you must acknowledge by name in both your code and your written assignments anyone with whom you worked. Failure to properly acknowledge your collaborators will result in initiation of academic integrity proceedings and failure of the assignment in question. Data Analysis Project (30%) You will be required to complete an independent data analysis project due the last week of class. You will choose among several data sets that I will provide for you and answer a series of questions about the data using the knowledge and skills built during the course. I see this as a way for you to leave this course with a concrete analysis project that you can keep in your portfolio to show potential employers. is project is to be done entirely independently; any discussion or collaboration with classmates, 2nd year students, other faculty, etc., will result in failure of the project and initiation of academic integrity proceedings. Final Exam (30%) e nal examwill be conducted remotely, and will be open-book and open-notes. Please note that there will be no early exam oered. Evaluations Bonus I care deeply about making this class better each year, but I cannot do that without your thoughtful input. is is especially important this year, given the possibility of future hybrid instruction. As an incentive, if 95% of the class has completed course evaluations before the second-to-last class session, I will drop everyone’s lowest homework grade fromnal grade calculations. Despite this encouragement, completing the evaluation is entirely your choice; no undue peer pressure to complete the evaluations will be tolerated. Final Grade Policy You must pass the homework, data analysis project, and nal exam portions of the class individually to pass the class as a whole. If you fail any of these three components, whether or not you pass the class will be considered on a case-by-case basis. As per GPS core course policy, QM2 nal grades are assigned on a curve with a median grade of B+ and a standard deviation le to my discretion. Prof. Francisco Garas Winter 2021 5 Course Policies Academic Accommodations If you have a disability that requires special testing accommodations or other classroom modications, you need to notify both the Oce for Students with Disabilities (OSD) and me within the rst two weeks of class. You may be asked to provide documentation of your disability to determine the appropriateness of accommodations. To notify the OSD, call (858) 534-4382 to schedule an appointment. Academic Integrity You are expected to comply with UCSD’s Policy on Integrity of Scholarship. In particular, plagiarism is con- sidered a dishonest practice and a serious academic oense. Hence, there will be a zero tolerance policy with respect to these practices: any student violating the obligation of academic integrity during the term will automatically fail the class. Collaborations are only allowed in homework assignments. However, even on homework assignments you must write up your results and your code independently (rule of thumb: if you are doing any copy-paste, you are likely not meeting this standard.) Tests, quizzes, and the independent analysis project must be done entirely by you. You will be allowed, but not required, to use outside sources to complete your independent analysis project, but you must properly acknowledge any sources that inuence your hypotheses, logic, rea- soning, model specication, interpretation, or conclusions. e independent analysis project must be done entirely on your own (e.g., no talking about it in the hallways or during a perhaps unrelated call). Failure to adhere to these standards will be considered plagiarism. Finally, you are not allowed to use course materials from previous years or to post solutions from this year’s assignments anywhere online or to pass them onto next year’s students. Failure to adhere to this policy will result in initiation of academic integrity proceedings, including aer the conclusion of the quarter. Copies of the current version of the UCSD Policy on Integrity of Scholarship, also commonly referred to as the Academic Dishonesty Policy, may be found on the Academic Senate webpage. Nondiscrimination and Harassment GPS and UC San Diego are committed to creating an environment in which all students are able to learn and openly express themselves in an environment free from all forms of discrimination and harassment. I encourage all students to read campus policy here. DevelopingWriting Skills You are encouraged to seek writing support in UCSD’s Writing Center. Please consider this resource, espe- cially if English is your second language. Prof. Francisco Garas Winter 2021 6 Sunday Monday Tuesday Wednesday Thursday Friday Saturday 3-Jan 4-Jan 5-Jan 6-Jan 7-Jan 8-Jan 9-Jan Week 1 10:00-10:50 AM East Warren Tent 10:00-10:50 AM East Warren Tent 7:00-8:20 PM QM2 Labs (remote) 2:00-3:20 PM/ 6:00-7:20 PM QM2 Labs (remote) HW1 Posted 10:00-10:50 AM East Warren Tent 10-Jan 11-Jan 12-Jan 13-Jan 14-Jan 15-Jan 16-Jan Week 2 10:00-10:50 AM East Warren Tent 10:00-10:50 AM East Warren Tent 7:00-8:20 PM QM2 Labs (remote) 2:00-3:20 PM/ 6:00-7:20 PM QM2 Labs (remote) 10:00-10:50 AM East Warren Tent 17-Jan 18-Jan 19-Jan 20-Jan 21-Jan 22-Jan 23-Jan Week 3 Dr. Martin Luther King, Jr. Day NO CLASS 10:00-10:50 AM East Warren Tent 7:00-8:20 PM QM2 Labs (remote) HW1 Due (10:00AM) 2:00-3:20 PM/ 6:00-7:20 PM QM2 Labs (remote) HW2 Posted 10:00-10:50 AM East Warren Tent 24-Jan 25-Jan 26-Jan 27-Jan 28-Jan 29-Jan 30-Jan Week 4 10:00-10:50 AM East Warren Tent 10:00-10:50 AM East Warren Tent 7:00-8:20 PM QM2 Labs (remote) 2:00-3:20 PM/ 6:00-7:20 PM QM2 Labs (remote) 10:00-10:50 AM East Warren Tent HW2 Due (10:00AM) 31-Jan 1-Feb 2-Feb 3-Feb 4-Feb 5-Feb 6-Feb Week 5 10:00-10:50 AM East Warren Tent 10:00-10:50 AM East Warren Tent 7:00-8:20 PM QM2 Labs (remote) HW3 Posted 2:00-3:20 PM/ 6:00-7:20 PM QM2 Labs (remote) 10:00-10:50 AM East Warren Tent 7-Feb 8-Feb 9-Feb 10-Feb 11-Feb 12-Feb 13-Feb Week 6 10:00-10:50 AM East Warren Tent 10:00-10:50 AM East Warren Tent 7:00-8:20 PM QM2 Labs (remote) 2:00-3:20 PM/ 6:00-7:20 PM QM2 Labs (remote) 10:00-10:50 AM East Warren Tent HW3 Due (10:00AM) 14-Feb 15-Feb 16-Feb 17-Feb 18-Feb 19-Feb 20-Feb Week 7 Presidents' DayNO CLASS HW4 Posted 10:00-10:50 AM East Warren Tent 7:00-8:20 PM QM2 Labs (remote) 2:00-3:20 PM/ 6:00-7:20 PM QM2 Labs (remote) 10:00-10:50 AM East Warren Tent 21-Feb 22-Feb 23-Feb 24-Feb 25-Feb 26-Feb 27-Feb Week 8 10:00-10:50 AM East Warren Tent 10:00-10:50 AM East Warren Tent 7:00-8:20 PM QM2 Labs (remote) HW4 Due (10:00AM) 2:00-3:20 PM/ 6:00-7:20 PM QM2 Labs (remote) 10:00-10:50 AM East Warren Tent 28-Feb 1-Mar 2-Mar 3-Mar 4-Mar 5-Mar 6-Mar Week 9 10:00-10:50 AM East Warren Tent 10:00-10:50 AM East Warren Tent 7:00-8:20 PM QM2 Labs (remote) 2:00-3:20 PM/ 6:00-7:20 PM QM2 Labs (remote) 10:00-10:50 AM East Warren Tent 7-Mar 8-Mar 9-Mar 10-Mar 11-Mar 12-Mar 13-Mar Week 10 10:00-10:50 AM East Warren Tent 10:00-10:50 AM East Warren Tent 7:00-8:20 PM QM2 Labs (remote) 2:00-3:20 PM/ 6:00-7:20 PM QM2 Labs (remote) IAP Due (11:59PM) 10:00-10:50 AM East Warren Tent 14-Mar 15-Mar 16-Mar 17-Mar 18-Mar 19-Mar 20-Mar Finals 3:00-6:00 PMFinal Exam 21-Mar 22-Mar 23-Mar 24-Mar 25-Mar 26-Mar 27-Mar Spring Break IAP and Final Exam Returned Grades Submitted Course Calendar Prof. Francisco Garas Winter 2021 7 Block Week Lecture Topics Reading Stata Commands Week 1 Course Introduction; Types of Validity, Introduction to Bivariate Linear Regression Wooldridge Ch.1 & Appendices A, B Miller- What are the Odds? Mastering Metrics- Intro & Chapter 1 use, save, keep, drop, log, clear, br, d, di, gen, su, ttest, graph, tw, set, do, corr Week 2 Gauss-Markov Assumptions; Hypothesis Testing Wooldridge Ch. 2.1-2.6 Derivations Notes Kennedy Ch. 1-3 (skip notes) encode, recode, label, destring, reg, predict, lfit, lfitci (schemes, scalars) Week 3 Interpretation; Alternative Functional Forms Gelman & Stern- Difference Between Significant & Not Significant if, in, foreach, forvalues, varlist, runiform, rnormal, ladder, qfit, qfitci (basic macros) Week 4 Omitted Variable Bias; Multiple Regression Wooldridge Ch. 3.1-3.6 margins, marginsplot Week 5 Mathematical Foundations; OLS in Matrix Notation Wooldridge Ch. 4-5 est* (mat functions) Week 6 Model Fit; Outliers; Qualitative Data Wooldridge Ch. 6 Science Isn't Broken (FiveThrityEight) egen, predict, i., lvr2plot, rvfplot, rvpplot, qqplot, pnorm Week 7 Interaction Effects Wooldridge Ch. 7 margins, marginsplot Week 8 Model Building; Measurement Error Wooldridge Ch. 9 test, cap, qui (workflow management) Week 9 Collinearity; Heteroskedasticity Wooldridge Ch. 8 King & Roberts- Robust Standard Errors reg, r vif, imtest, hettest Week 10 Endogeneity and the Experimental Ideal Review Wooldridge Ch. 9 Block 2: Basics of Multiple Regression Block 1: Foundations, Bivariate Model Block 3: Expanding Modeling Capabilities Block 4: Putting it all together, Common Estimation/Inference Problems Course Outline
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