Introduction and Overview

EC320, Set 01

Andrew Dickinson

Spring 2024

Prologue

Motivation

What is the goal of econometrics?

To learn about the world using data.

Why do economists (and others) study econometrics?

Providing answers to important problems.

Ex.

• Do minimum wage policies reduce poverty?
• Does the death penalty deter violent crime?
• How will global warming affect the economy?
• How responsive are polluter to a carbon tax?

• What explains the gender pay gap?
• Are recessions good for your health?
• Can we forecast the next recession?

Motivation

What is the goal of econometrics?

To learn about the world using data.

Why do economists (and others) study econometrics?

Providing answers to important problems.

How do you pronounce it?

uk · kaa · nuh · meh · truhks

Motivation

Why should you study econometrics?

Develop skills and learn to use tools that are valued by employers.

Cultivate a healthy sense of skepticism

IMO¹, of all the courses in a typical economics major, econometrics is the most translatable to a job

• Data is the new oil
• Extracting meaningful analysis from big data is a sought after skill in the job market of 2024

1. Please take this with a grain of salt. I am a single data point. Others may disagree

Motivation

Why should you study econometrics?

Throughout this course, I will try my best to emphasize why:

• Why are we learning this?
• Why does this matter with regard to future econometrics courses?
• Why is fill in the blank important for answering important problems?
• Why does this matter to employers?

Econometrics is built on crucial fundamentals. These fundamentals is the focus of this class.

uk · kaa · nuh · meh · truhks

Most econometric inquiry concerns one of two distinct goals:

1. Prediction: Accurately predict or forecast an outcome given a set of predictors. Given what we know about \(x\), what values do we expect \(y\) to take?

2. Causal identification: Estimate the effect of an intervention on an outcome. How does \(y\) change when we change \(x\)?

In this class, and in EC 421, we will focus on the later. The former is the focus of EC 422 and EC 524

Causal Identification

Causal identification

Common refrain.¹

“Correlation does not necessarily imply causation.”

Why might correlation fail to describe a causal relationship?

• Omitted-variables bias
• Selection bias
• Simultaneity

• Reverse causality
• Coincidence

1. Ex. Number of people who drowned by falling in pool and Nicolas Cage films

Causal identification

Common refrain.¹

“Correlation does not necessarily imply causation.”

Correlation may imply causation if we assume “all else equals”

• Hold everything fixed

This assumption is fragile in the real world.

Solutions:

• Conduct experiments

• Find a natural experiment

1. Ex. Number of people who drowned by falling in pool and Nicolas Cage films

Do you think this is a causal statement?

Experiments

How can we ensure the all else equals assumption holds?

Randomization

Randomized Controlled Trails (RCT)

• widely used across many scientific disciplines¹
• often touted as the gold standard of causal identification
• use randomization to ensure all else equals

In 2019, the Nobel Prize winners adapting RCTs to projects in development economics²

1. Medicine, psychology, education, agronomics among many others

2. Duflo, Banerjee, and Kremer

Experiments Ex.

Research question

Does health insurance improve health?

The all else equals assumption would require:

• all preexisting correlates with health must be the same across insured and uninsured

What would violate this assumption?

If more money is correlated with better health, and the average income of those who buy health insurance is higher, then we violate this assumption

Experiments Ex.

But what if health insurance is randomly assigned?

• Then, assuming the assignment is perfectly random across a large enough sample size, this assumption becomes much more palatable

Oregon Health Insurance Experiment

The Oregon Health Insurance Experiment is a landmark study of the effect of expanding public health insurance on health care use, health outcomes, financial strain, and well-being of low-income adults… In 2008, the state of Oregon drew names by lottery for its Medicaid program for low-income, uninsured adults, generating just such an opportunity. This ongoing analysis represents a collaborative effort between researchers and the state of Oregon to learn about the costs and benefits of expanding public health insurance.

Natural experiments

An external, non-experimental factor creates circumstances that resemble a controlled experiment

Real-world events provide opportunity to compare similar groups

With some assumptions, researchers infer the causal relationships examining differences in outcomes between groups

Natural experiments

Any examples of natural experiments that come to mind?

Here are some of the more famous ones:

1. Vietnam draft lottery

During the Vietnam War, the U.S. used a lottery system to determine which young men would be drafted into military service. The random assignment of draft numbers created a natural experiment that researchers have used to study the causal effects of military service on various outcomes, including earnings, education, and long-term health.

2. The Mariel Boatlift

The Mariel Boatlift: In 1980, Fidel Castro unexpectedly allowed around 125,000 Cubans to leave for the United States, creating a sudden increase in the supply of low-skilled labor in Miami. This “supply shock” has been used as a natural experiment to study the effects of immigration on the wages and employment opportunities of native workers in the receiving country.

3. Divorce Law Reforms

In the 1970s and 1980s, several U.S. states adopted “no-fault” divorce laws, which made it easier for couples to obtain a divorce without having to prove fault or misconduct by either spouse. These reforms created a natural experiment to study the effects of divorce law changes on marriage and divorce rates, labor force participation, and outcomes for children in affected families.

4. The Opening of the London Congestion Charge

In 2003, London introduced a congestion charge for vehicles entering the city center during peak hours. The charge aimed to reduce traffic congestion and improve air quality. The introduction of the charge provided a natural experiment to study its effects on air pollution, traffic congestion, and public transportation use.

In more recent news:

EC320

In EC320

We start to build up the fundamentals of causal analysis

But first we need to build up the necessary Theory, Tools, and Skills

This course will focus almost exclusively on a particular method that is common in statistics in general:

• Ordinary Least Squares (OLS) (aka linear regression)

Coursework

Rough weekly outline:

• 01: Introduction and review
• 02: The econometric problem
• 03: SLR estimation
• 04: SLR assumption
• 05: SLR inference

• 06: Midterm
• 07: MLR estimate and inference
• 08: Transformations
• 09: Quantitative variables
• 10: Exogeniety and final review

Final: Thursday, June 13 @ 08:00a

Syllabus

(click here)

Course site

I use GitHub to host a separate site with all the course materials

You can find a link to it here or on the Canvas homepage

I use it because:

1. it is convenient for me to post slides
2. it allows me to post class materials on my website
3. acts as a secondary site in case Canvas poops out

EVERYTHING will be posted to both Canvas and GitHub except one thing… the slides

All zoom records will only be available on Canvas

About me

Please call me Andrew

• Office: PLC 523
• Office hours: After class 1:20-2:00p and F 10:00-11:00a

• Email: adickin3@uoregon.edu

> Metrics

• I love studying econometrics
• My second time teaching EC320
• TA’d: EC421 (x2), EC422/522, EC423/523, EC424/524
• Instructed: EC320, EC330 (x3)

About me

Please call me Andrew

• Office: PLC 523
• Office hours: T & Th 4:00p-5:00p

• Email: adickin3@uoregon.edu

> Grad school

• 4th year Econ PhD student
• Applied topics related to environmental economics
• Causal inference, statistical learning, and data science
• Current focus on air pollution

About me

Please call me Andrew

• Office: PLC 523
• Office hours: T & Th 4:00p-5:00p

• Email: adickin3@uoregon.edu

> Before grad school

• Grew up in San Diego, CA
• Spent childhood/undergrad summers in the San Juan Islands
• Studied economics and math at San Diego State University
• Prior to PhD, researched crime and immigration topics

In EC320

An applied econometrician¹ needs a solid grasp on (at least) three areas:

1. The underlying theory (assumptions, strengths, weaknesses).
2. An ability to load, aggregating, joining, visualizing large datasets.
3. Applying the theoretical methods to actual data.

This course aims to deepen your knowledge in each of these three areas.

• 1: Analytical skills (Math)
• 2-3: Computational tools ()

1. Applied econometrician = Practitioner of econometrics, e.g., analyst, consultant, data scientist.

What is ?

To quote the project website¹

R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.

What does that mean?

• was created for the statistical and graphical work required by econometrics–written by statistical programmers

• has a vibrant, thriving online community. (stack overflow)

• Plus it’s free and open source

1. R wikipedia

Why are we using ?

1. is free and open source—saving both you and the university money.

2. Related: Outside of a small group of economists, private- and public-sector employers favor over Stata and most competing softwares.

3. is very flexible and powerful—adaptable to nearly any task, e.g., ’metrics, spatial data analysis, machine learning, web scraping, data cleaning, website building, teaching. I write all my slides, problem sets, and exams in R.

Why are we using ?

4. Related: imposes no artificial restrictions on your amount of observations, variables, memory, or processing power.

5. If you put in the work,¹ you will come away with a valuable and marketable tool.

6. I 💖

1. Learning definitely requires time and effort.

Getting started with

setup for EC 320

Installation

You need to install 2 pieces of software:

• RStudio

For explicit instructions for how to install, follow this tutorial

Note: /RStudio installations differ by operating system

R setup for EC 320

v. RStudio

• The programming language (ie english, spanish, french etc.)
• Ex. The engine, chassis, wheels, etc. of a car

• The Integrated Development Environment (IDE) (ie word processor)
• Ex. The dashboard containing various buttons and monitors

works without RStudio

RStudio doesn’t work without

R basics

You will dive deeper in lab, but here six big points about :

1. Everything is an object

2. Every object has a name and value

3. You use functions on these objects

4. Functions come in libraries (packages)

5. R will try to help you

6. R has its quirks

foo

foo <- 2

mean(foo)

library(dplyr)

?dplyr

NA; error; warning

Chat GPT

What is Chat GPT?

• Chat GPT is a language model developed by OpenAI.
• Based on the GPT-4 architecture.
• Trained on a diverse range of text sources.
• Capable of generating human-like responses.

Chat GPT and R Programming

• Useful for learning R syntax and best practices.
• Can provide code snippets and explanations.
• Helps in debugging and troubleshooting.
• Offers suggestions for data manipulation and analysis.

Chat GPT and Econometrics

• Assists with understanding econometric concepts.
• Provides examples of natural experiments and regression models.
• Explains various estimation methods and their assumptions.
• Helps with interpreting results and understanding their implications.

Limitations of Chat GPT

• Knowledge cutoff: September 2021.
• May not have the latest information on specific topics.
• Potential for generating incorrect or outdated information.
• Can sometimes provide verbose or irrelevant responses.

Chat GPT as a Learning Resource, Not for Cheating

• Use Chat GPT to deepen your understanding of the material.
• Cheating undermines your education and future success.
• Developing problem-solving skills is essential for long-term career growth.
• Engage with Chat GPT to clarify concepts, not to complete assignments.

Tips for Using Chat GPT Effectively

• Ask specific, well-defined questions.
• Always verify information provided by Chat GPT.
• Use multiple resources to cross-check and validate answers.
• Remember that Chat GPT is a tool to enhance your learning experience, not replace it.

Conclusion*

• Chat GPT can be a valuable resource for learning R programming and econometrics.
• Be aware of its limitations and always double-check the information provided.
• Use Chat GPT as a learning aid and not for cheating on assignments.
• Embrace the opportunity to develop problem-solving skills and deepen your understanding of the material.

Chat GPT

The previous 7 slides were all written by Chat GPT

GPT conclusion (written by me)

Chat GPT is a breathtaking piece of technology

But it is also frightening. This tech has and will continue to disrupt education

It has changed my day to day workflow already.

Use it wisely. Don’t cheat with it. But use it to help your understanding.

Next class: Statistics review

Table of contents

1. Prologue
2. Causal Identification
3. EC320
5. Chat GPT