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lectures/01-recap/recap1.Rmd

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1. Communication: Slack Intro
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2. Zoom: How to use
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2. Who Am I
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3. Who Am I
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3. This Course
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4. This Course
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5. Recap 1 of topics from intro course
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4. Recap 1 of topics from intro course
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]
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# Who Am I
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.pull-left[
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* I'm an Assistant Prof in the Dept of Economics at SciencesPo Paris. Check out my [website](https://floswald.github.io)!
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* I'm a Professor in the Dept of Economics at SciencesPo Paris. Check out my [website](https://floswald.github.io)!
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* I work on housing, urban, macro and labor topics:
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1. How to explain long run house price increase and city growth?
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* I work on urban, macro and IO topics:
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1. How to explain City Growth?
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2. How is fertility related to housing costs?
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3. What is the impact of Airbnb on long term rental markets?
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4. How do people decide to move from one place to another and what are the implications for labor and housing markets?
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3. How do people and firms decide where to locate?
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]
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--
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.pull-right[
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* I do a lot of computation (who doesn't). I like `R` (surprise!) and [`julia`](https://julialang.org) - I teach computational econ to our PhD students.
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* I profited *a lot* from the open source software (OSS) community (and some good friends), and I am fascinated by how it works and the huge value it creates for everyone.
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* OSS is key to reproducible research.
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* OSS is key to reproducible research. 👉 seeing that every day as [Data Editor](https://ejdataeditor.github.io)
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* I try to use and teach my students tools which enable greater reproducibility.
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* *my students* 👆 concerns **you**!
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]
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---
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* You are supposed to be familiar with all the econometrics material from [the slides](https://github.com/ScPoEcon/ScPoEconometrics-Slides) of that course and/or chapters 1-9 in our [textbook](https://scpoecon.github.io/ScPoEconometrics/).
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* We also assume you have basic `R` working knowledge at the level of the intro course! ()
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* We also assume you have basic `R` working knowledge at the level of the intro course!
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* basic `data.frame` manipulation with `dplyr`
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* simple linear models with `lm`
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* basic plotting with `ggplot2`
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* Quiz 1 will try and test for that 😉, so be on top of [this chapter](https://r4ds.had.co.nz/transform.html)
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* Quiz 1 will try and test for that 😉, so be on top of [this chapter](https://r4ds.hadley.nz/data-transform.html)
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---
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# Syllabus
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.pull-left[
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0. Intro, Difference-in-Differences
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0\. Intro, Difference-in-Differences
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1. Intro, Recap 1
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1\. Intro, Recap 1
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(*Quiz 1*)
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2. Recap 2
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2\. Recap 2
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(*Quiz 2*)
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3. Tools: `Rmarkdown` and `data.table`
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3\. Tools: `Rmarkdown` and `data.table`
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4. Instrumental Variables 1
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4\. Instrumental Variables 1
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(*Quiz 3*)
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5. Instrumental Variables 2
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5\. Instrumental Variables 2
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(*Midterm exam*)
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]
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$$ \hat{\beta}_0 = \overline{y} - \hat{\beta}_1 \overline{x} $$
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Remember that *those* two formulae are amongst the very few ones from the intro cours that you should know by heart! ❤️
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Remember that *those* two formulae are amongst the very few ones from the intro course that you should know by heart! ❤️
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--
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We now turn to the assumptions and (implied) properties of OLS.
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---
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---
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layout: true

lectures/01-recap/recap1.html

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### Florian Oswald
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]
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.date[
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### SciencesPo Paris </br> 2024-01-22
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### SciencesPo Paris </br> 2024-01-25
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]
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1. Communication: Slack Intro
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2. Zoom: How to use
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2. Who Am I
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3. Who Am I
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3. This Course
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4. This Course
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5. Recap 1 of topics from intro course
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4. Recap 1 of topics from intro course
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]
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# Who Am I
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.pull-left[
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* I'm an Assistant Prof in the Dept of Economics at SciencesPo Paris. Check out my [website](https://floswald.github.io)!
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* I'm a Professor in the Dept of Economics at SciencesPo Paris. Check out my [website](https://floswald.github.io)!
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* I work on housing, urban, macro and labor topics:
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1. How to explain long run house price increase and city growth?
87+
* I work on urban, macro and IO topics:
88+
1. How to explain City Growth?
9189
2. How is fertility related to housing costs?
92-
3. What is the impact of Airbnb on long term rental markets?
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4. How do people decide to move from one place to another and what are the implications for labor and housing markets?
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3. How do people and firms decide where to locate?
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]
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--
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.pull-right[
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* I do a lot of computation (who doesn't). I like `R` (surprise!) and [`julia`](https://julialang.org) - I teach computational econ to our PhD students.
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* I profited *a lot* from the open source software (OSS) community (and some good friends), and I am fascinated by how it works and the huge value it creates for everyone.
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* OSS is key to reproducible research.
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* OSS is key to reproducible research. 👉 seeing that every day as [Data Editor](https://ejdataeditor.github.io)
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* I try to use and teach my students tools which enable greater reproducibility.
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* *my students* 👆 concerns **you**!
105-
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]
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---
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* You are supposed to be familiar with all the econometrics material from [the slides](https://github.com/ScPoEcon/ScPoEconometrics-Slides) of that course and/or chapters 1-9 in our [textbook](https://scpoecon.github.io/ScPoEconometrics/).
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* We also assume you have basic `R` working knowledge at the level of the intro course! ()
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* We also assume you have basic `R` working knowledge at the level of the intro course!
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* basic `data.frame` manipulation with `dplyr`
120116
* simple linear models with `lm`
121117
* basic plotting with `ggplot2`
122-
* Quiz 1 will try and test for that 😉, so be on top of [this chapter](https://r4ds.had.co.nz/transform.html)
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* Quiz 1 will try and test for that 😉, so be on top of [this chapter](https://r4ds.hadley.nz/data-transform.html)
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---
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# Syllabus
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.pull-left[
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0. Intro, Difference-in-Differences
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0\. Intro, Difference-in-Differences
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1. Intro, Recap 1
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1\. Intro, Recap 1
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(*Quiz 1*)
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2. Recap 2
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2\. Recap 2
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(*Quiz 2*)
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3. Tools: `Rmarkdown` and `data.table`
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3\. Tools: `Rmarkdown` and `data.table`
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4. Instrumental Variables 1
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4\. Instrumental Variables 1
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(*Quiz 3*)
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5. Instrumental Variables 2
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5\. Instrumental Variables 2
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(*Midterm exam*)
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]
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$$ \hat{\beta}_0 = \overline{y} - \hat{\beta}_1 \overline{x} $$
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Remember that *those* two formulae are amongst the very few ones from the intro cours that you should know by heart! ❤️
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Remember that *those* two formulae are amongst the very few ones from the intro course that you should know by heart! ❤️
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--
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We now turn to the assumptions and (implied) properties of OLS.
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---
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---
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layout: true

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