ECON 4370 / 6370 Computing for Economics

.title[
# ECON 4370 / 6370 Computing for Economics
]
.subtitle[
## Lecture 0: Course Overview
]
.author[
### Zhan Gao
]
.date[
### 02 September 2025
]

---

## Overview
1. [Introduction](#3)
2. [Syllabus](#4)
3. [Software Installation](#20)
4. [R Basics](#26)

---

# Introduction

<div style="display: flex; align-items: center;">
  <img src="./images/logo.png" width="200" style="margin-right: 20px;">
  <div style="flex: 1; text-align: center;">
    <h3 style="margin: 0;">ECON 4370 / 6370 Computing for Economics</h3>
  </div>
</div>

.pull-left[
### Instructor
<svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M304 128a80 80 0 1 0 -160 0 80 80 0 1 0 160 0zM96 128a128 128 0 1 1 256 0A128 128 0 1 1 96 128zM49.3 464H398.7c-8.9-63.3-63.3-112-129-112H178.3c-65.7 0-120.1 48.7-129 112zM0 482.3C0 383.8 79.8 304 178.3 304h91.4C368.2 304 448 383.8 448 482.3c0 16.4-13.3 29.7-29.7 29.7H29.7C13.3 512 0 498.7 0 482.3z"/></svg> Zhan Gao ([website](https://zhan-gao.github.io/))
<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M64 112c-8.8 0-16 7.2-16 16v22.1L220.5 291.7c20.7 17 50.4 17 71.1 0L464 150.1V128c0-8.8-7.2-16-16-16H64zM48 212.2V384c0 8.8 7.2 16 16 16H448c8.8 0 16-7.2 16-16V212.2L322 328.8c-38.4 31.5-93.7 31.5-132 0L48 212.2zM0 128C0 92.7 28.7 64 64 64H448c35.3 0 64 28.7 64 64V384c0 35.3-28.7 64-64 64H64c-35.3 0-64-28.7-64-64V128z"/></svg> zhangao [at] smu [dot] edu
<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M464 256A208 208 0 1 1 48 256a208 208 0 1 1 416 0zM0 256a256 256 0 1 0 512 0A256 256 0 1 0 0 256zM232 120V256c0 8 4 15.5 10.7 20l96 64c11 7.4 25.9 4.4 33.3-6.7s4.4-25.9-6.7-33.3L280 243.2V120c0-13.3-10.7-24-24-24s-24 10.7-24 24z"/></svg> Office Hours: 1 - 3pm, Wednesday @ 301U, Umphrey Lee
]

.pull-right[
### Teaching Assistant
<svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M304 128a80 80 0 1 0 -160 0 80 80 0 1 0 160 0zM96 128a128 128 0 1 1 256 0A128 128 0 1 1 96 128zM49.3 464H398.7c-8.9-63.3-63.3-112-129-112H178.3c-65.7 0-120.1 48.7-129 112zM0 482.3C0 383.8 79.8 304 178.3 304h91.4C368.2 304 448 383.8 448 482.3c0 16.4-13.3 29.7-29.7 29.7H29.7C13.3 512 0 498.7 0 482.3z"/></svg> Zihao Yin
<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M64 112c-8.8 0-16 7.2-16 16v22.1L220.5 291.7c20.7 17 50.4 17 71.1 0L464 150.1V128c0-8.8-7.2-16-16-16H64zM48 212.2V384c0 8.8 7.2 16 16 16H448c8.8 0 16-7.2 16-16V212.2L322 328.8c-38.4 31.5-93.7 31.5-132 0L48 212.2zM0 128C0 92.7 28.7 64 64 64H448c35.3 0 64 28.7 64 64V384c0 35.3-28.7 64-64 64H64c-35.3 0-64-28.7-64-64V128z"/></svg> zihaoy [at] smu [dot] edu
<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M464 256A208 208 0 1 1 48 256a208 208 0 1 1 416 0zM0 256a256 256 0 1 0 512 0A256 256 0 1 0 0 256zM232 120V256c0 8 4 15.5 10.7 20l96 64c11 7.4 25.9 4.4 33.3-6.7s4.4-25.9-6.7-33.3L280 243.2V120c0-13.3-10.7-24-24-24s-24 10.7-24 24z"/></svg> Office Hours: 11 - 12pm, Monday
]

---

# Syllabus Highlights

(Read the full document on <span style="color:#CC0035">Canvas</span>.)

---

# Why this course?

Fill in the gaps left between traditional econometrics and methods classes and the real world problems.

- You need practical skills to <span style="color:#CC0035">implement</span> your ideas with <span style="color:#CC0035">real/simulated data</span>.
--
    ... on a *"computer"*
--
  - Computing skills are largely distinct from (and complementary to) the core 'metrics oeuvre familiar to economists
  - Data viz, cleaning and wrangling; simulation; cloud computation; relational databases; machine learning; etc
--

- Unfortunately, you still need to learn a programming language that the computer understands to get the job done<br>
--
&nbsp;&nbsp;&nbsp;&nbsp; ... even with the rapid advances of <span style="color:#CC0035">Generative AI</span> &nbsp;&nbsp; 
--
<div align="center">
<img src="https://media.tenor.com/pzpYwFLrKBkAAAAM/not-yet-hold-it.gif">
</div>

--
&nbsp;&nbsp;&nbsp;&nbsp; ... but maybe soon. Let's embrace it starting from this course

--
- That said, it is MORE important to know what you (or the AI model) are doing behind the scenes <br>
&nbsp;&nbsp;&nbsp;&nbsp;  ... you are the <span style="color:#CC0035">*manager*</span> who knows *what*, *how* and *WHY*

---

# Why this course?

<div align="center">
A <span class="font130" style="color:#354CA1">question</span> to be answered and/or a <span class="font130" style="color:#354CA1">decision</span> to be made
</div>

<div align="center">
<span class="font130" style="color:#354CA1">Data</span> <br>
numbers/text/images/etc. with <span style="color:#CC0035">context</span> <span class = "font80" style="color:gray30;">(David More &  George Cobb, 1997)</span><br>
Collection / retrieval / simulation <br>
cleaning / wrangling / visualization ...
</div>

<div align="center">
<span class="font130" style="color:#354CA1">Model</span> <br>
mathematical description of (1) the data generating process, (2) how does data depend on the model parameters
</div>

<div align="center">
<span class="font130" style="color:#354CA1">Computation</span> <br>
estimate parameters from data → <span class="font130" style="color:#CC0035">prediction, inference, decision</span> <br>
which answers the question and/or makes the decision
</div>

---

# Learning Outcomes

- Write and execute <span style="color:#CC0035">reproducible code</span> in <span style="color:#354CA1">R</span> for data analysis and visualization
  - <span style="color:#354CA1">Python</span> is perfectly fine ... but not used in the course materials
  - Choice of language is less and less important in the age of AI
- Use <span style="color:#354CA1">shell</span> tools, <span style="color:#354CA1">Git(hub)</span> for version control and collaborative workflows
- Collect, clean, and organize <span style="color:#CC0035">data</span> from diverse sources, including <span style="color:#CC0035">web scraping</span>, <span style="color:#CC0035">databases</span>, and public datasets
- Implement econometric and statistical methods for <span style="color:#CC0035">causal inference</span> and <span style="color:#CC0035">economic forecasting</span>
- Implement basic <span style="color:#CC0035">machine learning</span> algorithms
- Explore <span style="color:#CC0035">computing techniques</span> and <span style="color:#CC0035">modern computing infrastructure</span>
- Develop fully <span style="color:#CC0035">reproducible projects</span> including data, code and documentation

---

# You, at the end of this course

---

# Getting started

---

# Software installation and registration

1. Download [R](https://www.r-project.org/).

2. Download [RStudio](https://www.rstudio.com/products/rstudio/download/preview/).

3. Download [Git](https://git-scm.com/downloads).

4. Create an account on [GitHub](https://github.com/) and register for a student/educator [discount](https://education.github.com/discount_requests/new).

5. Join the class team on <span style="color:#CC0035">Teams</span>
--

If you had trouble completing any of these steps, please raise your hand.
- Go-to place for installation guidance and troubleshooting is Jenny Bryan's http://happygitwithr.com.

---

# Some OS-specific extras

I'll detail further software requirements as and when the need arises. However, to help smooth some software installation issues further down the road, please also do the following (depending on your OS):

- **Windows:** Install [Rtools](https://cran.r-project.org/bin/windows/Rtools/). I also recommend that you install [Chocolately](https://chocolatey.org/).
- **Mac:** Install [Homebrew](https://brew.sh/). I also recommend that you configure/open your C++ toolchain (see [here](https://github.com/rmacoslib/r-macos-rtools#installer-package-for-macos-r-toolchain-).)
- **Linux:** None (you should be good to go).

---

# Checklist

☑ Do you have the most recent version of R?
  
  ``` r
  version$version.string
  ```
  
  ```
  ## [1] "R version 4.5.1 (2025-06-13)"
  ```

☑ Do you have the most recent version of RStudio? (The [preview version](https://www.rstudio.com/products/rstudio/download/preview/) is fine.)
  
  ``` r
  RStudio.Version()$version
  ## Requires an interactive session but should return something like "[1] ‘1.4.1100’"
  ```

☑ Have you updated all of your R packages? 
  
  ``` r
  update.packages(ask = FALSE, checkBuilt = TRUE)
  ```
  
---

# Checklist (cont.)

Open up the [shell](http://happygitwithr.com/shell.html#shell).
- Windows users, make sure that you installed a Bash-compatible version of the shell. If you installed [Git for Windows](https://gitforwindows.org), then you should be good to go.

☑ Which version of Git have you installed?
  
  ``` bash
  git --version
  ```
  
  ```
  ## git version 2.39.5 (Apple Git-154)
  ```

☑ Did you introduce yourself to Git? (Substitute in your details.)
  
  ``` bash
  git config --global user.name 'Your Name'
  git config --global user.email 'your.email@smu.edu'
  git config --global --list
  ```

☑ Did you register an account in GitHub?

---

# Checklist (cont.)

We will make sure that everything is working properly with your R and GitHub setup next lecture.

For the rest of today's lecture, I want to go over some very basic R concepts with demonstrations.

</br>

PS — Just so you know where we're headed: We'll return to these R concepts (and delve much deeper) next week after a brief, but important detour to the lands of Git(Hub) and the shell.
- Don't worry, it will all make sense. You'll see.

---

# About R programming language

---

# What is R?

To quote the [R project website](https://www.r-project.org):

> 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?

- R was created for the statistical and graphical work required by econometrics.
- R has a vibrant, thriving online community. ([stack overflow](https://stackoverflow.com/questions/tagged/r))
- Plus it's **free** and **open source**.

---

# Why R?

- Many alternatives:
    - STATA v.s.R v.s. Python v.s MATLAB v.s. Julia v.s. C/C++
    - No clear answer...
- Advantages of R
    - R is <span style="color:#CC0035">free</span> and <span style="color:#CC0035">open source</span> (v.s. STATA and MATLAB)
    - Designed for data work. Favored by the academia, particular the statistics and econometrics community, which means most of the times you can apply most up-to-date methods with packages developed by the authors. (v.s. all others)
    - R is very <span style="color:#CC0035">flexible and powerful</span> — adaptable to nearly any task, *e.g.*, 'metrics, spatial data analysis, machine learning, web scraping, data cleaning, website building, teaching.
      - Well-developed environment. Rich sources of packages. (v.s. Julia.)
    - R imposes <span style="color:#CC0035">no limitations</span> on your amount of observations, variables, memory, or processing power. (v.s. Stata)
    - If you put in the work, you will come away with a <span style="color:#CC0035">valuable and marketable</span> tool.

---

# Python?

- ... but ... path dependencies of econoemtricians and statisticians 
- You got to master one of them first<br>
--
&nbsp;&nbsp;&nbsp;&nbsp; ... and you will be using both of them in the real world

---

# Why R? (cont.)

and you <span style="color:#CC0035">have to </span> learn one of them

---
# Additional resources

- Official Manual
    - [R-Introduction](https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf)
- Books
    - Grolemund G. [Hands-On Programming with R](https://rstudio-education.github.io/hopr/)
    - Hanck, C., Arnold, M., Gerber, A., and Schmelzer, M. [Econometrics with R](https://www.econometrics-with-r.org/)
- SMU Office of Information Technology Help Desk
- Jenny Bryan's [Happy Git and Github for the useR](http://happygitwithr.com)

---

# Next lecture(s): R programming