Introduction to ECON 8070

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.title[
# Introduction to ECON 8070
]
.author[
### Brantly Callaway
]
.date[
### University of Georgia
]

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# A Brief Introduction to ECON 8070

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The econometrics sequence at UGA has dual goals:

* Provide a foundation for doing empirical work in economics / business disciplines

* Provide an introduction/foundation to econometrics research itself

In general, Ph.D. level course are different from undergraduate/M.A. level courses:

* We will be interested not just in what to do but also how things work

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# A Brief Introduction to ECON 8070

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# A Brief Introduction to ECON 8070

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# A Brief Introduction to ECON 8070

Some examples of learning about how things work:

- For all the estimators that we talk about this semester, we will write the code to implement them ourselves rather than relying on "canned" implementations from `R`.
 
 - We'll also spend a lot of time on theory related to conducting inference &mdash; this can be mathematically intense

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# A Brief Introduction to ECON 8070

Throughout the semester, we'll also mostly be interested on learning tools/concepts that are useful for conducting empirical research in economics (and, more broadly, social sciences and business disciplines)

* In practice, a whole lot of research questions are like: how did some policy/intervention/etc. cause some outcomes of interest to change relative to what they would have been in the absence of the policy/intervention?

* We will have these sorts of questions in mind (or at least in the back of our minds) throughout the semester

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# Econometrics Course Sequence at UGA

* ECON 8070, Probability and Statistics (Callaway)

- err...Probability, Statistics, Linear Regression

* ECON 8080, Introduction to Econometrics (Callaway)

- Linear Regression (cont'd), Causal Inference, Panel Data

* ECON 8110, Econometrics I (Caetano)

- Instrumental Variables, Generalized Method of Moments, Regression Discontinuity, Nonparametric Regression

--
    
* ECON 8120, Econometrics II (Kinsler)

- Nonlinear Models, Structural Econometrics
    
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# Statistical Programming

This semester, we'll do a lot of statistical programming, using the `R` language

- Please see the syllabus for additional resources on `R` programming

- Our TA, Brad, is a very good R programmer, and he is a very good resource this semester

- I expect the difficulty level on this front to be relatively high

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# Other Resources

On the syllabus, there are a number of additional resources.  These are mostly at the Ph.D. or M.A. level.  If you need more introductory material on any topics here are some suggestions:

- I have [very detailed course notes](https://bcallaway11.github.io/econ_4750_notes/) for my undergraduate class (both on `R` and on Econometrics)

- A relatively short introduction to `R` from Stephanie Spielman: <a href="https://sjspielman.github.io/datascience_for_biologists/tutorials/introduction_to_R.html">https://sjspielman.github.io/datascience_for_biologists/tutorials/introduction_to_R.html</a>

- I also like <a href="https://www.amazon.com/Probability-Statistical-Inference-10th-Robert/dp/013518939X/ref=sr_1_3?keywords=hogg+tanis+zimmerman&qid=1660677502&sprefix=hogg+tanis%2Caps%2C68&sr=8-3">Hogg, Tanis, and Zimmerman</a> as an undergraduate-level probability and statistics text <a href="https://www.amazon.com/Introduction-Econometrics-4th-Pearson-Economics/dp/0134461991/ref=sr_1_1?keywords=stock+and+watson+introduction+to+econometrics&qid=1641821555&sprefix=stock+and+wat%2Caps%2C157&sr=8-1">Stock and Watson</a> and <a href="https://www.amazon.com/Introductory-Econometrics-Modern-Approach-MindTap/dp/1337558869/ref=sr_1_1?keywords=wooldridge+econometrics&qid=1641821581&sprefix=wooldridge+%2Caps%2C86&sr=8-1">Wooldridge</a> as undergraduate level econometrics texts.

- They both cover a lot of the same material that we will cover in this course, though at an easier level. 
  
  - [If you get any of these, you can get an older edition and save some money.]

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# Last comment before we get going...

My super-high-level advice about research:

* It may be easy to get lost in the math/programming this semester

* It's true that there are big technical hurdles that you need to go over

* But a lot of econometrics / empirical work in economics is fundamentally about having good ideas, intuition, and clear thinking...you just have to get over the technical hurdle