1. Course description

Application of statistical methods to estimating and testing economic relationships, i.e., econometric techniques. Topics include heteroskedasticity, clustered standard errors, instrumental variables, simultaneous equations, quantile regression, maximum likelihood, discrete and limited dependent variables, panel data methods, nonparametric estimation, and some popular machine learning methods. By the end of the course students should be able to understand the uses and limits of  these different statistical procedures and implement them in R.

This class has a mandatory lab component. In the lab sessions students will learn how to code in R in order to be able to solve the assignments and the final project required for Econ 114. The objective of the lab sessions is to teach the fundamentals of R programming. The first lab sessions will focus on coding primitives (data structures, control flow, etc.) and subsequent sessions will teach the necessary tools to implement the methods taught in lecture.

2. Prerequisite(s)

Econ 100A or Econ 100M, and Econ 113.

3. Teaching Team

Instructor: Julian Martinez-Iriarte, [email protected]

TA: Thinkling Li, [email protected]

4. Lectures

In Person: Tuesday and Thursday, 3:20-4:55PM, Earth&Marine B214

5. Secondary Section

The lab discussion section will be offered twice on Zoom in real time twice on Fridays: at 9:20- 10:25AM, and at 12:00-01:05PM. Students must attend one secondary section every week.

6. Office Hours TBC

7. Optional Textbooks

We will follow Introductory Econometrics by Jeffrey M. Wooldridge (on reserve in the library). The lab sessions will be structured around Using R for Introductory Econometrics by Florian Heiss (freely available here). Other useful references are Mastering ‘Metrics and Mostly Harmless Econometrics by Joshua D. Angrist and Jörn-Steffen Pischke. An excellent source for machine learning topics is An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (freely available here).

8. Course Website

Canvas.

9. Grades

Multiple-Choice (15%): There will be 4 multiple choice assignments during the quarter. They are designed for students to test their programming knowledge in R.

Assignments (35%): There will be 4 assignments during the quarter. They provide an opportunity to practice methods and develop intuition. Assignments must be turned in at the start of class. This due date is specified in each homework. Assignments turned in up to 24hs after the due date will be penalized with a reduction equal to 15 percentage points of the maximum possible grade. Assignments turned in after these 24hs will receive a zero.

1-Page Draft (5%): Students should present around week 7, a 1-page draft/proposal of their final project.

Final Project (45%): The final project is an independent research project that can be either theoretical or empirical, or both. You can investigate a topic of your choice that uses the methods that you learned in the class. You should write up a research paper that has the structure of an article in a peer-reviewed academic journal. The paper should be at least 15 pages double-spaced, 12-point font, 1-inch margins on all sides, and no more than 30 pages. The due date for the paper is Friday, December 9 ay 1:00PM. Assessment will be based on the adequacy of the econometric tools used to answer the particular question.

10. Software

We will use R, which is freely available here: https://www.r-project.org.

11. Workload Expectations

The median student can expect to spend, on average, 17.5 hours per week on this class. Weekly workload is divided as follow:

· 190 minutes (3.17 hours) in lecture,

· 65 minutes (1.08 hours) of secondary lab,

· 5 hours in each assignment/1-page draft (each assignment takes around 10 hours)

· 0.5 hours in Multiple Choice (each multiple choice should take around 1 hour)

· 5.25 hours reading

Additionally, in weeks 8 through 10, the median student is expected to spend a total of 25 hours working on the final project.

12. Course Reading

Week 0: Th, Sept 22 Lecture: Review of Probability and Random Sampling (Wooldridge Appendix B and C).

Week 1: Tu, Sept 27 Lecture: Review of Probability Distributions and Introduction to Monte Carlo Experiments (Wooldridge Appendix B and C).

Th, Sept 29 Lecture: Review of Sample Mean, LLN, CLT and Confidence Intervals (Wooldridge Appendix C).

Secondary Section: Basics of R Programming, Data Manipulation and Descriptive Statistics (Heiss Sections 1.2, 1.3, 1.4 and 1.6).

Week 2: Tu, Oct 4 Lecture: OLS Estimation, Asymptotic Distribution, and Inference (Wooldridge Sections 3.2,3.3, 4.1, 4.2, 4.3, 5.1, and 5.2)

Th, Oct 6 Lecture: Heteroskedasticity Robust Inference (Wooldridge Sections 8.1 and 8.2). Secondary Section: Implementation of OLS in R, Monte Carlo Simulations, Multiple Linear Regression and Heteroskedastic Robust Inference (Heiss Sections 2.1, 2.2, 2.7, 3.1, and 8.1).

Week 3: Tu, Oct 11 Lecture: Another look at OLS, IV Estimators (Wooldridge Sections 15.1, 15.2, 15.4). Th, Oct 13 Lecture: Measurement Error, Examples of Endogeneity, Supply and Demand Estimation (Wooldridge Sections 9.3, 16.1, and 16.2).

Secondary Section: Solve Assignment 1.

Week 4: Tu, Oct 18 Lecture: Two-Stages Least Squares, Additional Topics in IV (Weak and Invalid

IV) (Wooldridge Sections 15.1 and 15.3).

Th, Oct 20 Lecture: OLS vs IV vs 2SLS, Inference in 2SLS (Wooldridge Section 15.1, 15.3 and 15.6).

Secondary Section: Implement IV Estimators in R (Heiss Sections 15.1, 15.2, and 15.3).

Week 5: Tu, Oct 25 Lecture: Simultaneous Equations and Simple Panel Data Methods (Wooldridge Sections 16.3, 13.1, 13.3, and 13.4).

Th, Oct 27 Lecture: Differences-in-Differences (Wooldridge Sections 13.2 and 13.4). Secondary Section: Solve Assignment 2.

Week 6: Tu, Nov 1 Lecture: Advanced Panel Data Methods (First Difference and Fixed Effects Estimation) (Wooldridge Sections 14.1, 14.2, and 14.3).

Th, Nov 3 Lecture: Limited Dependent Variables, Linear Probability Models, Introduction to Logit and Probit Models (Wooldridge Sections 7.5, 7.6, and 17.1).

Secondary Section: Panel Data Methods in R (Heiss Sections 13.1, 13.2, 13.5, and 14.1).

Week 7: Tu, Nov 8 Lecture: Logit and Probit Models, Tobit Model (Wooldridge Sections 17.1 and 17.2). Th, Nov 10 Lecture: Sample Selection, Introduction to Time Series Methods, Trends and Seasonality (Wooldridge Sections 17.4, 17.5, 10.3, and 10.5).

Secondary Section: Introduce “dplyr” and “ggplot2” packages (Heiss Section 1.5).

Week 8: Tu, Nov 15 Lecture: How to Carry out an Empirical Project, Examples of Previous’ Years Empirical Projects (Wooldridge Chapter 19)

Th, Nov 17 Lecture: Moving Average, Autoregressive Process, OLS Estimation of AR(1), Efficient Market Hypothesis (Wooldridge Sections 11.1, 11.2, Example 11.4).

Secondary Section: Solve Assignment 3.

Week 9: Tu, Nov 22 Lecture: Weakly Dependent Time Series, MA(1), AR(1), OLS Asymptotics, Highly Persistent Time Series (Wooldridge Sections 11.1, 11.2, 11.3)

Th, Nov 24 Lecture: Thanksgiving Holiday

Secondary Section: Serial Correlation-Inference, ARCH, Forecasting in R (Heiss Sections 12.3,

12.4, and 18.5).

Week 10: Tu, Nov 29 Lecture: Serial Correlation, Durbin-Watson Test, FGLS, ARCH Model (Wooldridge Sections 12.1, 12.2, 12.3, and 12.5).

Th, Dec 1 Lecture: Forecasting, Granger Causality, Spurious Regression (Wooldridge Sections 18.3 and 18.5).

Secondary Section: Solve Assignment 4.

13. Academic Integrity

All work submitted for this class must be your own. Collaboration on assignments is encouraged, but the answers you submit must be your own and based on your own understanding. Copying answers or existing papers is a violation of university policy. Here http://guides.library.ucsc. edu/citesources you can learn how to properly cite resources you consulted. For more information on academic integrity at UC Santa Cruz, please visit https://ue.ucsc.edu/academic-misconduct.

14. Accessibility - DRC

UC Santa Cruz is committed to creating an academic environment that supports its diverse student body. If you are a student with a disability who requires accommodations to achieve equal access in this course, please affiliate with the DRC. I encourage all students to benefit from learning more about DRC services to contact DRC by phone at 831-459-2089 or by email at [email protected]. For students already affiliated, make sure that you have requested Academic Access Letters, where you intend to use accommodations. You can also request to meet privately with me during my office hours or by appointment, as soon as possible. I would like us to discuss how we can implement your accommodations in this course to ensure your access and full engagement in this course.

15. Title IX

The Title IX Office is committed to fostering a campus climate in which members of our community are protected from all forms of sex discrimination, including sexual harassment, sexual violence, and gender-based harassment and discrimination. Title IX is a neutral office committed to safety, fairness, trauma- informed practices, and due process. Title IX prohibits gender discrimination, in- cluding sexual harassment, domestic and dating violence, sexual assault, and stalking. If you have experienced sexual harassment or sexual violence, you can receive confidential support and advo- cacy at the Campus Advocacy Resources & Education (CARE) Office by calling (831) 502-2273. In addition, Counseling & Psychological Services (CAPS) can provide confidential, counseling support, (831) 459-2628. You can also report gender discrimination directly to the University’s Title IX Of- fice, (831) 459-2462. Reports to law enforcement can be made to UCPD, (831) 459-2231 ext. 1. For emergencies call 911. Please visit https://titleix.ucsc.edu for more information.