Syllabus - Economics 103 & 103L - Spring 2024
Course Description Econ 103 and Econ 103L
It seeks to make students effective consumers and producers of empirical research in economics. The entire course is based on the study of the linear regression model.
The tentative topics to be covered include:
● Econ 41 Review. Single and multiple random variables. Properties of the normal distribution. The sample mean as a random variable. Inference on the mean: confidence intervals and hypothesis testing.
● Simple Regression. (Principles of Econometrics Chapters 2, 3, and 4). Linear regression with one regressor: The estimator and the estimand. Inference in linear regression: confidence intervals and hypothesis testing. Prediction, goodness of fit, and modelling choices.
● Multiple Regression. (Principles of Econometrics Chapters 5 and 6). Linear regression with multiple regressors: The estimator and the estimand. Inference, testing joint hypotheses, and model specification.
● Additional Topics. (Principles of Econometrics Chapters 7, 8, and 10). Treatment effects and the differences in differences model. Heteroskedasticity. Instrumental Variables and estimation via moment conditions.
Econ 103 is our main course. It consists of lectures that explain the theory of linear regressions. All lectures will be held on Tuesdays and Thursdays, at Econ 103L is our the and This course provides an introduction to the theory and practice of econometrics. The course comprises both Econ 103 and Econ 103L.
Lab Lectures - Econ 103L
The 103L lectures will cover the basics of coding in R (no prior knowledge will be assumed). We will have lectures almost every Tuesday, from 5:00 PM until 5:50 PM.
All lectures and office hours will be held online using Zoom:
Join Zoom Meeting https://ucla.zoom.us/j/91311734917 Zoom Meeting ID 913 1173 4917
You will need a Computer and access to the internet in order to follow the lecture. A recorded video of the lab lectures will posted in the Canvas site of our 103 course.
Lab lectures are essential for you to understand how to obtain empirical evaluations discussed in Econ 103. The specific codes we will use will not be asked in any of the exams. The most important part of the course is to understand the underlying theory of econometric evaluations and the interpretation of regression outputs. There are several softwares that can be used to perform. econometric evaluations.
The particular language used by these softwares vary and it is not the goal of the course to verify if you know R or any other software such as Stata. The goal of the course is to form econometricians instead of users of statistical softwares. We intend to avoid Lab Lectures close to the Mid-term or the Final Exam.
The grade you obtain in our Econ 103 course will also be your grade for Econ 103L.
Knowledge Prerequisites
The course requires the student to have a good understanding of statistical concepts and tools discussed in the course Econ 41. The course also requires the student to have some basic mathemati- cal knowledge regarding first order derivatives, summations, logarithms, integrals, and exponentials. The formal requisites for the course are: courses 11, and 41 or Mathematics 170A and 170B or 170E and 170S or Statistics 100A and 100B.
Main Textbook
Main book for the course Principles of Econometrics. Hill, R. C., Griffiths, W. E. and G. C. Lim, 4th Edition, 2011.
The pdf of some chapters and exercises of the book are available in the course site.
Additional Textbooks
Other textbooks that you may find useful include:
● Introductory Econometrics. Wooldridge, J., 5th Edition, 2013, South Western.
● Introduction to Econometrics. Stock and Watson, 4th Edition, 2010, Pearson Press.
Econometric Software
This course will use primarily the open-source statistical software called R. The software is usually regarded as the most complete and well-known statistical and econometric software in the world. We will learn the basics of coding in R. Some excellent free resources are:
● Hand-On Programming with R. Grolemund:
https://rstudio-education.github.io/hopr/
● R for Data Science. Grolemund and Wickham: https://r4ds.had.co.nz/
● Introduction to Econometrics with R. Hanck, Arnold, Gerber, and Schmelzer: https://www.econometrics-with-r.org/index.html
● Principles of Econometrics with R (Tutorial) . Colonescu: https://bookdown.org/ccolonescu/RPoE4/
● Principles of Econometrics with R (Book) . Colonescu: https://bookdown.org/ccolonescu/RPoE4/RPoE.pdf
Jupyter Notebooks
We will use R in Jupyter, which is a online platform. It is both a text editor that enables you to write reports as well as a console that enables you to code and run in R. For a basic introduction of Jupyter Notebook, visit the site: https://jupyter-notebook.readthedocs.io/en/stable/ examples/Notebook/Notebook%20Basics.html
Accessing Jupyter
To access the Jupyter hub, you must go to the website
https://econ103.oarc.ucla.edu
and sign in using your UCLA ID and log on.
It is best to create a folder, say “Econ 103” . You will then need to upload the Jupyter Notebooks that will be posted in our Canvas site as the course progresses.
You will be able to change and save your notebooks in the Jupyter hub. The problem sets will be posted as Notebooks. You will have to print your problem sets into a pdf archive to submit to your TA.
Alternatively, you can use jupyter online for free using the program Anaconda, via the site https://anaconda.org/.
Grading
The overall grade for the course will be computed as follows:
1. 20% Homework (Problem Sets)
The tentative number of problem sets are 5, the tentative schedule is presented below. The problem sets will be posted as Jupyter Notebook in our Canvas site. You will need to: save the problem set in your Jupyter hub, solve the problem set, save the solution as a pdf and submit your answers in the assignment section of our site. The problem sets can be submitted until midnight of the due-date.
2. 30% Mid-term Exam (in class, Tuesday, May 14, 2023, 2:00 PM - 3:15 PM - Dodd Hall Room 147)
There will be an optional mid-term. If you choose to take it, then the mid-term grade will account for 20% of your course grade as long as that grade is higher than your final grade. If your final grade is higher, then only the final grade will count.
The mid-term exam will cover all the material taught in the classes before the exam.
3. 50% Final Exam (Cumulative, Tuesday, June 12, 2023, 8:00 AM - 11:00 AM)
Instructions on how to attend the final exam will be given in the week prior to the exam. The final exam will cover all the material taught in the course.
The final exam will consist only of multiple-choice questions. Grading of the final exam depends on whether you take the mid-term exam or not:
● Final exam will account for 80% of your grad if you choose not to take the mid-term exam, or if the mid-term grade is below the final exam grade.
● Final exam will account for 50% of your grad if the mid-term grade is higher than the grade of the final exam.