class: center, middle, inverse, title-slide .title[ # What is Econometrics? ] .author[ ### Brantly Callaway ] .date[ ### University of Georgia ] --- # What will we learn this semester? `$$\newcommand{\E}{\mathbb{E}}$$` <style type="text/css"> .alert { font-weight:bold; color: red; } .alert-blue { font-weight: bold; color: blue; } .remark-slide-content { font-size: 23px; padding: 1em 4em 1em 4em; } .highlight-red { background-color:red; padding:0.1em 0.2em; } </style> At a very high level, our goal for this semester is to develop skills related to <span class="alert">using data</span> to <span class="alert-blue">learn something</span> -- The three main topics that we will consider this semester are: - Linear regression - We'll become experts on this topic -- - Modern approaches to <span class="alert">prediction</span> using machine learning -- - Modern approaches to <span class="alert">causal inference</span> --- # What will we learn this semester? Because this is an advanced course, I also have the goal of learning both <span class="alert">the mechanics</span> of Econometrics *and* <span class="alert-blue">how it works</span> -- <center><img src="lawnmower1a.jpeg", width="400px", height="300px"> -- <img src="lawnmower2a.jpg", width="400px", height="300px"></center> --- # 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 -- --- # 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 --- # Data Availability in Economics In statistics classes (and, for example, lab sciences), researchers often have access to <span class="alert">experimental data</span> -- - 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 --- # 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 --- # Econometrics and Economic Theory Economics, as a discipline, has a rich history in economic theory. -- - Utility / profit maximization - Supply / Demand -- - Most economic models involve <span class="alert">tradeoffs</span> (no free lunch, etc.) - Econometrics can be useful for weighing tradeoffs --- # Econometrics and Economic Theory The "theory" part of much empirical work in economics is often the <span class="alert">key step</span> -- - "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 --- # Econometrics and Economic Theory At a minimum, we'll develop skills to <span class="alert">think critically</span> 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 --- # 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 <span class="alert">data collection</span> - This is usually not glamorous but important --- # 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? --- # 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" --- # 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/) --- # Election <center><img src="data:image/png;base64,#election-map.png"/></center> --- # 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] --- # Example 2: Union Wage Premium <span class="alert">Idea/Trick:</span> - 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 --- # Dinardo and Lee (QJE, 2004) <center><img src="data:image/png;base64,#union_premium_rd.png"/></center>