Econometrics - Panel data and endogeneity

class: center, middle, inverse, title-slide

.title[
# Econometrics - Panel data and endogeneity
]
.subtitle[
## Course Introduction
]
.author[
### Jonas Björnerstedt, Xiaoying Li
]
.date[
### 2024-02-16
]

---

## Introduction

- Rules of the game
    - Course will use and follow the textbook
        - Lectures and slides to help you
        - Exam questions will be similar to book
    - R examples during lectures
    - Individual submission of computer exercises required
        - Work on Computer Exercises together, but write your own!
        - Do book exercises together
- Point of the course?
    - Handle data, plot and estimate
        - knowledge of central concepts
    - Applied course
        - But with intuition for theory
    - Different backgrounds
        - Repetition good even if you know the stuff

---
## Course requirements

- Examination
  - Final exam 80%
  - Assignment 20%

- Lectures
  - 10 lectures

#### Prerequisites

- Probability and statistics
    -  Ch. 2. Review of Probability
    -  Ch. 3. Review of Statistics
    
Lecture notes available on site.

---
## Readings

- Stock and Watson, Introduction to Econometrics, 4th (or Updated 3rd edition)
  - Parts 1, 2 and 3
  
- [Introduction to Econometrics with R](https://www.econometrics-with-r.org/) 
    - Stock & Watson using R
    
- New textbook very similar
  - Page numbers etc. will refer to the 4th edition

---
## Resources

- Course web: https://rstudio.sh.se
  - Most resources are __not__ posted on the studieweb
  - Not practical with many resources

- Do not ask course questions on the Studieweb
  - I check once a week or so...

---
## Other resources

- The internet!
    - Google for questions asked on www.stackexchange.com and similar sites
    - Wikipedia is very good in probability and statistics
    - [Khan academy](https://www.khanacademy.org/math/statistics-probability)
        - From basic to advanced with [an app with videos and exercises](https://itunes.apple.com/us/app/khan-academy-you-can-learn/id469863705?mt=8)
- If you find something, share it!
  - [An example](https://mgimond.github.io/ES218/index.html)

---
## Course outline

- Univariate regression
    -  Ch. 4. Linear Regression with One Regressor 
    -  Ch. 5. Hypothesis Tests and Confidence Intervals   
- Multivariate regression
    -  Ch. 6. Linear Regression with Multiple Regressors  
    -  Ch. 7. Hypothesis Tests and Confidence Intervals 
    - Ch. 8. Nonlinear regression functions
    - Ch. 9. Assessing studies based on multiple regression
- Microeconometrics 
    - Ch  12. Instrumental Variable Regression
    - Ch. 10. Regression with panel data 
    - Ch. 11. Regression with a Binary Dependent Variable 
    - Ch. 13. Experiments and Quasi-Experiments

---
## Three alternative R environments

- Four ways of getting R and Rstudio!
  1. Install on your own computer
  1. The course datalab: https://rstudio.sh.se/datalab
  1. https://rstudio.cloud 
  1. SH computer labs(?)
  
- Easier to provide help in datalab
  - Installed versions can have problems
  - The course datalab is temporary!

---
## Installing Rstudio

Both _R_ and _RStudio_ have to be downloaded and installed

* Download R
    * [R for Windows](https://cran.r-project.org/bin/windows/base/)
    * [R för Mac](https://cran.r-project.org/bin/macosx/)
* [Get Rstudio](https://www.rstudio.com/products/rstudio/download/)