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`@@ -18,6 +18,7 @@ kernelspec:`
`18`	`18`	`</div>`
`19`	`19`	```
`20`	`20`
	`21`	`+(ar1_processes)=`
`21`	`22`	`# AR1 Processes`
`22`	`23`
`23`	`24`	```{admonition} Migrated lecture
`@@ -29,10 +30,6 @@ This lecture has moved from our [Intermediate Quantitative Economics with Python`
`29`	`30`	```{index} single: Autoregressive processes
`30`	`31`	```
`31`	`32`
`32`		-```{contents} Contents
`33`		`-:depth: 2`
`34`		-```
`35`		`-`
`36`	`33`	`## Overview`
`37`	`34`
`38`	`35`	`In this lecture we are going to study a very simple class of stochastic`

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`16`	`16`	```{index} single: The Perron-Frobenius Theorem
`17`	`17`	```
`18`	`18`
`19`		-```{contents} Contents
`20`		`-:depth: 2`
`21`		-```
`22`		`-`
`23`	`19`	`In addition to what's in Anaconda, this lecture will need the following libraries:`
`24`	`20`
`25`	`21`	```{code-cell} ipython3

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`14`	`14`	`(heavy_tail)=`
`15`	`15`	`# Heavy-Tailed Distributions`
`16`	`16`
`17`		-```{contents} Contents
`18`		`-:depth: 2`
`19`		-```
`20`		`-`
`21`	`17`	`In addition to what's in Anaconda, this lecture will need the following libraries:`
`22`	`18`
`23`	`19`	```{code-cell} ipython3

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`17`	`17`	```{index} single: Markov Chains: Basic Concepts and Stationarity
`18`	`18`	```
`19`	`19`
`20`		-```{contents} Contents
`21`		`-:depth: 2`
`22`		-```
`23`		`-`
`24`	`20`	`In addition to what's in Anaconda, this lecture will need the following libraries:`
`25`	`21`
`26`	`22`	```{code-cell} ipython3

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`16`	`16`	```{index} single: Markov Chains: Irreducibility and Ergodicity
`17`	`17`	```
`18`	`18`
`19`		-```{contents} Contents
`20`		`-:depth: 2`
`21`		-```
`22`		`-`
`23`		`-`
`24`	`19`	`In addition to what's in Anaconda, this lecture will need the following libraries:`
`25`	`20`
`26`	`21`	```{code-cell} ipython3

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`17`	`17`	```{index} single: Distributions and Probabilities
`18`	`18`	```
`19`	`19`
`20`		-```{contents} Contents
`21`		`-:depth: 2`
`22`		-```
`23`		`-`
`24`	`20`	`## Outline`
`25`	`21`
`26`	`22`	`In this lecture we give a quick introduction to data and probability distributions using Python.`

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`@@ -17,11 +17,8 @@ kernelspec:`
`17`	`17`	`</div>`
`18`	`18`	```
`19`	`19`
`20`		-# {index}`Dynamics in One Dimension <single: Dynamics in One Dimension>`
`21`		`-`
`22`		-```{contents} Contents
`23`		`-:depth: 2`
`24`		-```
	`20`	`+(scalar_dynam)=`
	`21`	`+# Dynamics in One Dimension`
`25`	`22`
`26`	`23`	`## Overview`
`27`	`24`

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`28`	`28`	```{index} single: Models; Schelling's Segregation Model
`29`	`29`	```
`30`	`30`
`31`		-```{contents} Contents
`32`		`-:depth: 2`
`33`		-```
`34`		`-`
`35`	`31`	`## Outline`
`36`	`32`
`37`	`33`	`In 1969, Thomas C. Schelling developed a simple but striking model of racial`

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`23`	`23`	```{index} single: Dynamic Programming; Shortest Paths
`24`	`24`	```
`25`	`25`
`26`		-```{contents} Contents
`27`		`-:depth: 2`
`28`		-```
`29`		`-`
`30`	`26`	`## Overview`
`31`	`27`
`32`	`28`	`The shortest path problem is a [classic problem](https://en.wikipedia.org/wiki/Shortest_path) in mathematics and computer science with applications in`

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`14`	`14`	`(supply_demand_heterogeneity)=`
`15`	`15`	`# Market Equilibrium with Heterogeneity`
`16`	`16`
`17`		-```{contents} Contents
`18`		`-:depth: 2`
`19`		-```
`20`		`-`
`21`	`17`	`## Overview`
`22`	`18`
`23`	`19`	In the {doc}`previous lecture