Introduction and Overview

---

# Prologue

---
# Why?

## Motivation

Let's start with a few __basic, general questions:__

1. What is the goal of econometrics?

2. Why do economists (or other people) study or use econometrics?

__One simple answer:__ Learn about the world using data.

- _Learn about the world_ = Raise, answer, and challenge questions, theories, assumptions.

- _data_ = Plural of datum.

---
# Why?

## Example

One might (reasonably) guess a company's .purple[sales] are a function of its .pink[advertising spending, price, and intesity of competitors].

So, one might hypothesize a model `$\color{#6A5ACD}{\text{Sales}} = f(\color{#e64173}{\text{Ad}, \text{Price}, \text{Comp}})$`

where

- `$\color{#e64173}{\text{Ad}}$` represents dollars spent on advertising,
- `$\color{#e64173}{\text{Price}}$` is the product's price,
- `$\color{#e64173}{\text{Comp}}$` gives the product's competition.

We expect that .purple[sales] `$\uparrow$` with .pink[advertising] and `$\downarrow$` with .pink[price] and .pink[competition].

---
class: clear, middle

But who needs to .grey[_expect_]?

We can .orange[_test_] these hypotheses __using regression__.

- *How much* does an additional dollar of advertising increase sales?
- *How much* does a one-dollar increase in price decrease sales?
- *How much* does an additional competitor reduce sales?

These (causal) questions are central to efficient decision-making<br>and are the bread and butter of econometrics.
]

---
class: clear, middle

But who needs to .grey[_expect_]?

We can .orange[_test_] these hypotheses __using regression__.

- *How much* does an additional dollar of .pink[advertising] increase .purple[sales]?
- *How much* does a one-dollar increase in .pink[price] decrease .purple[sales]?
- *How much* does an additional .pink[competitor] reduce .purple[sales]?

These (causal) questions are central to efficient decision-making<br>and are the bread and butter of econometrics.

---
layout: true
# Why?

## Example, cont.

__Regression model:__

$$ \color{#6A5ACD}{\text{Sales}}_i = \beta_0 + \beta_1 \color{#e64173}{\text{Ad}}_i + \beta_2 \color{#e64173}{\text{Price}}_i + \beta_3 \color{#e64173}{\text{Comp}}_i + \varepsilon_i $$

---

With this basic regression model, we can test/estimate/quantify the (linear) relationship between sales and advertising, price, and competition.

---

### (Review) Questions

- __Q:__ How do we interpret `$\beta_1$`?
--

- __A:__ An additional dollar of .pink[advertising] corresponds with a `$\beta_1$`-unit change in .purple[sales] (holding .pink[price] and .pink[competition] fixed).

---

### (Review) Questions

- __Q:__ Are the `$\beta_k$` terms population parameters or sample statistics?
--

- __A:__ Greek letters denote __population parameters__. Their estimates get hats, _e.g._, `$\hat{\beta}_k$`. Population parameters represent the __average__ behavior across the population.

---

### (Review) Questions

- __Q:__ Can we interpret the estimates for `$\beta_2$` as causal?
--

- __A:__ Not without making more assumptions and/or knowing more about the data-generating process.

---

### (Review) Questions

- __Q:__ What is `$\varepsilon_i$`?
--

- __A:__ An individual's random deviation/disturbance from the population parameters.

Population parameters are averages; individuals are rarely average.

---

### (Review) Questions

- __Q:__ Which assumptions do we impose when estimating with OLS?
--

- __A:__
  - The relationship between the sales and the .pink[explanatory variables] is linear in parameters, and `$\varepsilon$` enters additively.
  - The .pink[explanatory variables] are __exogenous__, _i.e._, `$E[\varepsilon|X] = 0$`.
  - You've also typically assumed something along the lines of:<br> `$E[\varepsilon_i] = 0$`, `$E[\varepsilon_i^2] = \sigma^2$`, `$E[\varepsilon_i \varepsilon_j] = 0$` for `$i \neq j$`.
  - And (maybe) `$\varepsilon_i$` is distributed normally.

---
layout: false

# Assumptions

## How important can they be?

You've learned how **powerful and flexible** ordinary least squares (**OLS**) regression can be.

However, the results you learned required assumptions.

**Real life often violates these assumptions.**

---
class: clear, middle

EC421 asks "**What happens when we violate these assumptions?**"
- Can we find a fix? (Especially: How/when is `$\beta$` *causal*?)
- What happens if we don't (or can't) apply a fix?

OLS still does some amazing things—but you need to know when to be **cautious, confident, or dubious**.

---

# Not everything is causal

---
# Not everything is causal
## More seriously

Suppose you estimate our sales model for your boss.

$$ \color{#6A5ACD}{\text{Sales}}_i = \hat{\beta}_0 + \hat{\beta}_1 \color{#e64173}{\text{Ad}}_i + \hat{\beta}_2 \color{#e64173}{\text{Price}}_i + \hat{\beta}_3 \color{#e64173}{\text{Comp}}_i + e_i $$

Can you trust that `$\hat{\beta}_2$` gives you the actual effect of .pink[price] on .purple[sales]?

---

# Econometrics

Applied econometrics, data science, analytics require:

1. Intuition for the __theory__ behind statistics/econometrics<br>(assumptions, results, strengths, weaknesses).

1. Practical knowledge of how to __apply theoretical methods__ to data.

1. Efficient methods for __working with data__<br>(cleaning, aggregating, joining, visualizing).

__This course__ aims to deepen your knowledge in each of these three areas.

- 1: As before.
- 2–3: __R__

---
class: inverse, middle
# R

---
layout: true
# R

---

## 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 are we using R?

1\. R is __free__ and __open source__—saving both you and the university 💰💵💰.

2\. _Related:_ Outside of a small group of economists, private- and public-sector __employers favor R__ over .mono[Stata] and most competing softwares.

3\. R 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. My website, the TWEEDS website, and these notes all came out of R.

---

## Why are we using R?

4\. _Related:_ R imposes __no limitations__ on your amount of observations, variables, memory, or processing power. (I'm looking at __you__, .mono[Stata].)

5\. If you put in the work,<sup>†</sup> you will come away with a __valuable and marketable__ tool.

6\. I 💖 __R__

---
<img src="slides_files/figure-html/statistical languages-1.svg" style="display: block; margin: auto;" />

---

---

# R + Regression

``` r
# A simple regression
*fit <- lm(dist ~ 1 + speed, data = cars)
# Show the coefficients
coef(summary(fit))
```

```
#>               Estimate Std. Error   t value     Pr(>|t|)
#> (Intercept) -17.579095  6.7584402 -2.601058 1.231882e-02
#> speed         3.932409  0.4155128  9.463990 1.489836e-12
```

``` r
# A nice, clear table
library(broom)
tidy(fit)
```

```
#> # A tibble: 2 × 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)   -17.6      6.76      -2.60 1.23e- 2
#> 2 speed           3.93     0.416      9.46 1.49e-12
```

---

# R + Plotting (w/ .mono[plot])

---

# R + Plotting (w/ .mono[plot])

``` r
# Load packages with dataset
library(gapminder)

# Create dataset
plot(
  x = gapminder$gdpPercap, y = gapminder$lifeExp,
  xlab = "GDP per capita", ylab = "Life Expectancy"
)
```

---

# R + Plotting (w/ .mono[ggplot2])

---

# R + Plotting (w/ .mono[ggplot2])

``` r
# Load packages
library(gapminder); library(dplyr)

# Create dataset
ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
geom_point(alpha = 0.75) +
scale_x_continuous("GDP per capita", label = scales::comma) +
ylab("Life Expectancy") +
theme_pander(base_size = 16)
```

---

# R + More plotting (w/ .mono[ggplot2])

---

# R + More plotting (w/ .mono[ggplot2])

``` r
# Load packages
library(gapminder); library(dplyr)

# Create dataset
ggplot(
  data = filter(gapminder, year %in% c(1952, 2002)),
  aes(x = gdpPercap, y = lifeExp, color = continent, group = country)
) +
geom_path(alpha = 0.25) +
geom_point(aes(shape = as.character(year), size = pop), alpha = 0.75) +
scale_x_log10("GDP per capita", label = scales::comma) +
ylab("Life Expectancy") +
scale_shape_manual("Year", values = c(1, 17)) +
scale_color_viridis("Continent", discrete = T, end = 0.95) +
guides(size = F) +
theme_pander(base_size = 16)
```

---

# R + Animated plots (w/ .mono[gganimate])

---

# R + Animated plots (w/ .mono[gganimate])

``` r
# The package for animating ggplot2
library(gganimate)
# As before
ggplot(
  data = gapminder %>% filter(continent != "Oceania"),
  aes(gdpPercap, lifeExp, size = pop, color = country)
) +
geom_point(alpha = 0.7, show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
scale_x_log10("GDP per capita", label = scales::comma) +
facet_wrap(~continent) +
theme_pander(base_size = 16) +
theme(panel.border = element_rect(color = "grey90", fill = NA)) +
# Here comes the gganimate-specific bits
labs(title = "Year: {frame_time}") +
ylab("Life Expectancy") +
transition_time(year) +
ease_aes("linear")
```

---

# R + Maps

``` r
library(leaflet)
leaflet() %>%
  addTiles() %>%
  addMarkers(lng = -123.075, lat = 44.045, popup = "The University of Oregon")
```

<div class="leaflet html-widget html-fill-item" id="htmlwidget-788449e9a08574bcde66" style="width:756px;height:432px;"></div>
<script type="application/json" data-for="htmlwidget-788449e9a08574bcde66">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addTiles","args":["https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"https://openstreetmap.org/copyright/\">OpenStreetMap<\/a>,  <a href=\"https://opendatacommons.org/licenses/odbl/\">ODbL<\/a>"}]},{"method":"addMarkers","args":[44.045,-123.075,null,null,null,{"interactive":true,"draggable":false,"keyboard":true,"title":"","alt":"","zIndexOffset":0,"opacity":1,"riseOnHover":false,"riseOffset":250},"The University of Oregon",null,null,null,null,{"interactive":false,"permanent":false,"direction":"auto","opacity":1,"offset":[0,0],"textsize":"10px","textOnly":false,"className":"","sticky":true},null]}],"limits":{"lat":[44.045,44.045],"lng":[-123.075,-123.075]}},"evals":[],"jsHooks":[]}</script>

---
class: inverse, middle
# Getting started with R

---
layout: true
# Starting R

---

## Installation

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

- Install [.mono[RStudio]](https://www.rstudio.com/products/rstudio/download/preview/).

- __Optional/Overkill:__ [Git](https://git-scm.com/downloads)
  - Create an account on [GitHub](https://github.com/)
  - Register for a student/educator [discount](https://education.github.com/discount_requests/new).
  - For installation guidance and troubleshooting, check out Jenny Bryan's [website](http://happygitwithr.com/).

- __Note:__ Many UO labs have R installed and ready. That said, having a copy of R on your own computer will likely be very convenient for homework, projects, _etc._

---

## Resources

### Free(-ish)

- Google (which inevitably leads to StackOverflow)
- Time
- ChatGPT, Copilot, and other AI assistants
- [Data services at the UO library](https://library.uoregon.edu/data-services)
- Your classmates
- Your GE and me
- R resources [here](http://edrub.in/ARE212/resources.html) and [here](https://education.rstudio.com/)
- [swirl](https://swirlstats.com/) and [learnr](https://cran.r-project.org/web/packages/learnr/index.html)

### Money

Short online courses, e.g., [DataCamp](https://www.datacamp.com)

---

## Some R basics

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

1. Everything is an __object__.

1. Every object has a __name__ and __value__.

1. You use __functions__ on these objects.

1. Functions come in __libraries__ (__packages__)

1. R will try to __help__ you.

1. R has its __quirks__.

]

`foo`

`foo = 2`

`mean(foo)`

`library(dplyr)`

`?dplyr`

`NA; error; warning`

]

---
exclude: true

## R _vs._ .mono[Stata]

Coming from .mono[Stata], here are a few important changes (benefits):

- Multiple objects and arrays (_e.g._, data frames) can exist in the same workspace (in memory). No more `keep`, `preserve`, `restore`, `snapshot` nonsense!

- (Base) R comes with lots of useful built-in functions—and provides all the tools necessary for you to build your own functions. However, many of the _best_ functions come from external libraries.

- You don't need to `tset` or `xtset` data (you can if you really want... `ts`).

---
layout: false
class: inverse, middle

# Next: (More) Metrics review(s)