What is Econometrics?

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.title[
# What is Econometrics?
]
.author[
### Brantly Callaway
]
.date[
### University of Georgia
]

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# What will we learn this semester?

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At a very high level, our goal for this semester is to develop skills related to using data to learn something

The three main topics that we will consider this semester are:

- Linear regression

- We'll become experts on this topic

--
 
- Modern approaches to prediction using machine learning

- Modern approaches to causal inference

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# What will we learn this semester?

Because this is an advanced course, I also have the goal of learning both the mechanics of Econometrics *and* how it works

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# What will we learn this semester?

In order to get to where we want to go, we will have to learn some tools along the way:

- Statistical programming

- In this course, we will use the `R` programming language
  
  - This is an important skill in its own right
  
--
  
- Crash course in Probability and Statistics

- This may be a review
  
  - But it is the "language" we'll be speaking this semester
  
--

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# Econometrics vs. Statistics?

- Certainly, there is a lot of overlap

- Main differences:

- We'll focus on economic questions
  
  - Different data availability
  
  - Influenced by economic theory

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# Data Availability in Economics

In statistics classes (and, for example, lab sciences), researchers often have access to experimental data

- Ex: If a chemist is interested in studying the effect of "more heat" on some chemical process, they can often directly manipulate the amount of heat

- Ex: A doctor studying the effect of some new medicine can randomly assign some patients to take it and assign others a placebo

In some ways, this makes statistics in some disciplines not-too-complicated

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# Data Availability in Economics

In economics, we very rarely have access to experimental data

- Ex. Hard to imagine that you could convince countries to randomly assign their:

- interest rate, immigration policy, patent policy, anti-trust laws, minimum wage, etc.
  
  - though all of these are of substantial interest to economists
  
  - and we often have quite a bit of related data

We'll have to think quite carefully about how we can use existing data to learn in these settings

- ...or if we can learn anything in these settings

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# Econometrics and Economic Theory

Economics, as a discipline, has a rich history in economic theory.

- Utility / profit maximization

- Supply / Demand

- Most economic models involve tradeoffs (no free lunch, etc.)

- Econometrics can be useful for weighing tradeoffs
  
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# Econometrics and Economic Theory

The "theory" part of much empirical work in economics is often the key step

- "Confirmation bias"

- Patterns in data that are "surprising" often have other explanations ("Correlation is not causation")

- Ex. Classical music and baby's IQ
  
--
  
- But other explanations often "matter" for results that "make sense" too

- Ex. Education and wages
  
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# Econometrics and Economic Theory

At a minimum, we'll develop skills to think critically about empirical research and/or empirical claims

"Fixing" these critiques can be more challenging, but we'll:

1. Learn some "tricks"

2. Learn to be precise about potential weaknesses of our own research

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# Economic Data

One of the exciting things about studying Econometrics is that there is a lot of available data

Examples:

- GDP of different countries [[Link: World Bank Indicators]](http://databank.worldbank.org/data/reports.aspx?Code=NY.GDP.PCAP.CD&id=af3ce82b&report_name=Popular_indicators&populartype=series&ispopular=y)
- Micro data from Census Bureau [[Link]](https://usa.ipums.org/usa/)
- Micro data from Current Population Survey [[Link]](https://cps.ipums.org/cps/)
- Micro data on demographics and health in developing countries [[Link]](https://www.idhsdata.org/idhs/)
- Annual Survey of Manufactures [[Link]](https://www.census.gov/programs-surveys/asm.html)

That being said, a lot of important contributions to our understanding of the economy have come from data collection

- This is usually not glamorous but important

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# Goals for the Econometrics

1. Prediction / Forecasting
  - If bank makes a loan to a firm, how likely are they to pay it back?
  - What do we think that U.S. GDP will be in 2022?
  - Predicting Covid-19 cases across locations/time periods
  
--
	
2. Policy Analysis / Causal Inference
  - What was the effect of increasing the minimum wage on employment?
	- Did Covid-19 lockdowns affect the number of Covid-19 cases, employment, etc.?
	- How much does smoking affect health outcomes?
	
--
	
3. Counterfactuals
  - What would happen if the Fed increased the interest rate?
  - What would happen if patent protections were decreased to 10 years?
  - What would happen if Georgia increased its minimum wage?
	
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# Two Examples of Econometrics

Prediction

- 2016 Presidential Election
  
  - I like this example because it is an example of not-very-good predictions
  
  - But in hindsight, it is fairly easy to see why
  
Causal Inference

- Causal Effect of Union Membership on Wages/Employment

- This is an example of "regression discontinuity"

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# Example 1: Predicting Elections

**2016 Election Forecast**

* NY Times: Trump 15%, Clinton 85%
* Huffington Post: Trump 1.7%, Clinton 98.0%
* Five Thirty Eight: Trump 28.6%, Clinton 71.4%
* Princeton Election Consortium: Trump 1%, Clinton 99%

[[Link: Interesting Read on Why Most Election Forecasts Did Poorly]](http://fivethirtyeight.com/features/why-fivethirtyeight-gave-trump-a-better-chance-than-almost-anyone-else/)

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# Election

<center><img src="data:image/png;base64,#election-map.png"/></center>
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# Example 2: Union Wage Premium

On average, union members earn about 15% more than non-union members with similar characteristics (education, demographic characteristics, etc.)

This could be the causal effect ("union premium")

But there are also other potential explanations

- Perhaps, among observationally similar individuals, union members tend to have higher productivity

- Unions may tend to organize at relatively successful firms ("threat effects")

[This "debate" may seem esoteric, but there are many researchers that think very carefully about these sorts of issues]

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# Example 2: Union Wage Premium

Idea/Trick:

- Whether a company becomes unionized is typically based on a secret-ballot vote by its employees

- Compare very close "successful" union elections to "unsuccessful" elections

- By construction, these companies should be "similar" to each other

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# Dinardo and Lee (QJE, 2004)