New lectures and TOC reorg (#197)

* Reorganization of lectures with new intro ones added

* Added names

* Fix to links

* wealth dynamics compiling

* Added wealth dynamics references

* Fixed document references

* Fixes issue #146

* Added in benchmarking

* Fixed the `p` argument

* Figures complete

* Some cleanup and additional results

* Minor edits

Co-authored-by: James Yu <[email protected]>
Co-authored-by: Dawie van Lill <[email protected]>

Author: 3 people

lectures/Project.toml

@@ -1,5 +1,5 @@
 name = "quantecon-notebooks-julia"
-authors = ["arnavs <me@arnavsood.com>"]
+authors = ["quantecon <jesseperla@gmail.com>"]
 version = "0.8.0"
 
 [deps]

lectures/_static/quant-econ.bib

@@ -468,6 +468,15 @@ @article{benhabib2015
     publisher={Elsevier}
 }
 
+@article{benhabib2018skewed,
+  title   = {Skewed wealth distributions: Theory and empirics},
+  author  = {Benhabib, Jess and Bisin, Alberto},
+  journal = {Journal of Economic Literature},
+  volume  = {56},
+  number  = {4},
+  pages   = {1261--91},
+  year    = {2018}
+}
 @book{puterman2005,
     title={Markov decision processes: discrete stochastic dynamic programming},
     author={Puterman, Martin L},
@@ -525,6 +534,16 @@ @article{Aiyagari1994
     year    = {1994}
 }
 
+@article{Gabaix2009,
+author = {Gabaix, Xavier},
+title = {Power Laws in Economics and Finance},
+journal = {Annual Review of Economics},
+volume = {1},
+number = {1},
+pages = {255-294},
+year = {2009},
+doi = {10.1146/annurev.economics.050708.142940},
+}
 @article{amss2002,
   author={S. Rao Aiyagari and Albert Marcet and Thomas J. Sargent and Juha Seppala},
   title={{Optimal Taxation without State-Contingent Debt}},

lectures/_toc.yml

@@ -30,17 +30,22 @@ parts:
   - file: tools_and_techniques/linear_algebra
   - file: tools_and_techniques/orth_proj
   - file: tools_and_techniques/lln_clt
-  - file: tools_and_techniques/ar1_processes
-  - file: tools_and_techniques/linear_models
-  - file: tools_and_techniques/finite_markov
   - file: tools_and_techniques/stationary_densities
-  - file: tools_and_techniques/kalman
   - file: tools_and_techniques/numerical_linear_algebra
   - file: tools_and_techniques/iterative_methods_sparsity
+- caption: Introduction to Dynamics
+  numbered: true
+  chapters:
+  - file: introduction_dynamics/scalar_dynam  
+  - file: introduction_dynamics/ar1_processes
+  - file: introduction_dynamics/finite_markov  
+  - file: introduction_dynamics/linear_models
+  - file: introduction_dynamics/wealth_dynamics
+  - file: introduction_dynamics/kalman
+  - file: introduction_dynamics/short_path
 - caption: Dynamic Programming
   numbered: true
   chapters:
-  - file: dynamic_programming/short_path
   - file: dynamic_programming/mccall_model
   - file: dynamic_programming/mccall_model_with_separation
   - file: dynamic_programming/wald_friedman

lectures/about_lectures.md

@@ -195,6 +195,6 @@ skills, and the many others who have contributed suggestions, bug fixes or
 improvements. They include but are not limited to Anmol Bhandari, Long Bui,
 Jeong-Hun Choi, David Evans, Xiaojun Guan, Shunsuke Hori, Chenghan Hou, Doc-Jin Jang, Adam Jozefiak,
 Qingyin Ma, Akira Matsushita, Tomohito Okabe, Daisuke Oyama, David Pugh, Alex
-Olssen, Nathan Palmer, Pooya Rashidi Ravari, Arnav Sood, Bill Tubbs, Natasha Watkins, Pablo Winant, Kaan Yolsever and Yixiao
+Olssen, Nathan Palmer, Pooya Rashidi Ravari, Arnav Sood, Bill Tubbs, Dawie van Lill, Natasha Watkins, Pablo Winant, Kaan Yolsever, James Yu, and Yixiao
 Zhou.
 

lectures/dynamic_programming/discrete_dp.md

@@ -56,7 +56,7 @@ This lecture covers
 
 We use dynamic programming many applied lectures, such as
 
-* The {doc}`shortest path lecture <../dynamic_programming/short_path>`
+* The {doc}`shortest path lecture <../introduction_dynamics/short_path>`
 * The {doc}`McCall search model lecture <../dynamic_programming/mccall_model>`
 * The {doc}`optimal growth lecture <../dynamic_programming/optgrowth>`
 
@@ -579,7 +579,7 @@ Another interesting object is `results.mc`, which is the controlled chain define
 
 In other words, it gives the dynamics of the state when the agent follows the optimal policy.
 
-Since this object is an instance of MarkovChain from  [QuantEcon.jl](http://quantecon.org/quantecon-jl) (see {doc}`this lecture <../tools_and_techniques/finite_markov>` for more discussion), we
+Since this object is an instance of MarkovChain from  [QuantEcon.jl](http://quantecon.org/quantecon-jl) (see {doc}`this lecture <../introduction_dynamics/finite_markov>` for more discussion), we
 can easily simulate it, compute its stationary distribution and so on
 
 ```{code-cell} julia

lectures/dynamic_programming/lqcontrol.md

@@ -39,7 +39,7 @@ Moreover, while the linear-quadratic structure is restrictive, it is in fact far
 
 These themes appear repeatedly below.
 
-Mathematically, LQ control problems are closely related to {doc}`the Kalman filter <../tools_and_techniques/kalman>`.
+Mathematically, LQ control problems are closely related to {doc}`the Kalman filter <../introduction_dynamics/kalman>`.
 
 * Recursive formulations of linear-quadratic control problems and Kalman filtering problems both involve matrix **Riccati equations**.
 * Classical formulations of linear control and linear filtering problems make use of similar matrix decompositions (see for example {doc}`this lecture <../time_series_models/lu_tricks>` and {doc}`this lecture <../time_series_models/classical_filtering>`).
@@ -48,7 +48,7 @@ In reading what follows, it will be useful to have some familiarity with
 
 * matrix manipulations
 * vectors of random variables
-* dynamic programming and the Bellman equation (see for example {doc}`this lecture <../dynamic_programming/short_path>` and {doc}`this lecture <../dynamic_programming/optgrowth>`)
+* dynamic programming and the Bellman equation (see for example {doc}`this lecture <../introduction_dynamics/short_path>` and {doc}`this lecture <../dynamic_programming/optgrowth>`)
 
 For additional reading on LQ control, see, for example,
 
@@ -350,7 +350,7 @@ What's special about the LQ case is that -- as we shall soon see ---  the optima
 ### Solution
 
 To solve the finite horizon LQ problem we can use a dynamic programming
-strategy based on backwards induction that is conceptually similar to the approach adopted in {doc}`this lecture <../dynamic_programming/short_path>`.
+strategy based on backwards induction that is conceptually similar to the approach adopted in {doc}`this lecture <../introduction_dynamics/short_path>`.
 
 For reasons that will soon become clear, we first introduce the notation $J_T(x) = x' R_f x$.
 

lectures/dynamic_programming/mccall_model.md

@@ -403,7 +403,7 @@ To understand why, first recall that `v_iv` is a function argument -- either def
 
 As usual, we are better off using a package, which may give a better algorithm and is likely to less error prone.
 
-In this case, we can use the `fixedpoint` algorithm discussed in {doc}`our Julia by Example lecture <../getting_started_julia/julia_by_example>`  to find the fixed point of the $T$ operator.
+In this case, we can use the `fixedpoint` algorithm discussed in {doc}`our Julia by Example lecture <../getting_started_julia/julia_by_example>`  to find the fixed point of the $T$ operator.  Note that below we set the parameter `m=1` for Anderson iteration rather than leaving as the default value - which fails to converge in this case.  This is still almost 10x faster than the `m=0` case, which corresponds to naive fixed-point iteration.
 
 ```{code-cell} julia
 function compute_reservation_wage(params; v_iv = collect(w ./(1-β)), iterations = 500,
@@ -412,7 +412,7 @@ function compute_reservation_wage(params; v_iv = collect(w ./(1-β)), iterations
     T(v) = max.(w/(1 - β), c + β * E*v) # (5) fixing the parameter values
 
     v_star = fixedpoint(T, v_iv, iterations = iterations, ftol = ftol,
-                        m = 0).zero # (5)
+                        m = 1).zero # (5)
     return (1 - β) * (c + β * E*v_star) # (3)
 end
 ```

lectures/dynamic_programming/optgrowth.md

@@ -38,7 +38,7 @@ The model is a version of the standard one sector infinite horizon growth model
 The technique we use to solve the model is dynamic programming.
 
 Our treatment of dynamic programming follows on from earlier
-treatments in our lectures on {doc}`shortest paths <../dynamic_programming/short_path>` and
+treatments in our lectures on {doc}`shortest paths <../introduction_dynamics/short_path>` and
 {doc}`job search <../dynamic_programming/mccall_model>`.
 
 We'll discuss some of the technical details of dynamic programming as we

lectures/dynamic_programming/perm_income.md

@@ -138,7 +138,7 @@ The consumer also faces initial conditions $b_0$ and $y_0$, which can be fixed o
 
 For the remainder of this lecture, we follow Friedman and Hall in assuming that $(1 + r)^{-1} = \beta$.
 
-Regarding the endowment process, we assume it has the {doc}`state-space representation <../tools_and_techniques/linear_models>`.
+Regarding the endowment process, we assume it has the {doc}`state-space representation <../introduction_dynamics/linear_models>`.
 
 ```{math}
 :label: sprob15ab
@@ -388,7 +388,7 @@ Then we can express equation  {eq}`pi_ssr` as
 \end{aligned}
 ```
 
-We can use the following formulas from {doc}`linear state space models <../tools_and_techniques/linear_models>` to compute population mean $\mu_t = \mathbb{E}    x_t$ and covariance $\Sigma_t := \mathbb{E} [ (x_t - \mu_t) (x_t - \mu_t)']$
+We can use the following formulas from {doc}`linear state space models <../introduction_dynamics/linear_models>` to compute population mean $\mu_t = \mathbb{E}    x_t$ and covariance $\Sigma_t := \mathbb{E} [ (x_t - \mu_t) (x_t - \mu_t)']$
 
 ```{math}
 :label: lss_mut_perm_income
@@ -663,7 +663,7 @@ Equation {eq}`sprob77`  asserts that the *cointegrating residual*  on the left s
 ### Cross-Sectional Implications
 
 Consider again {eq}`sprob16abcd`, this time in light of our discussion of
-distribution dynamics in the {doc}`lecture on linear systems <../tools_and_techniques/linear_models>`.
+distribution dynamics in the {doc}`lecture on linear systems <../introduction_dynamics/linear_models>`.
 
 The dynamics of $c_t$ are given by
 
@@ -932,7 +932,7 @@ In the same discussion in {cite}`Ljungqvist2012` it is shown that $K \in [0,1]$
 
 In other words, $K$ increases as the ratio of the standard deviation of the permanent shock to that of the transitory shock increases.
 
-Please see  {doc}`first look at the Kalman filter <../tools_and_techniques/kalman>`.
+Please see  {doc}`first look at the Kalman filter <../introduction_dynamics/kalman>`.
 
 Applying formulas {eq}`sprob16abcd` implies
 

lectures/dynamic_programming/perm_income_cons.md

@@ -51,7 +51,7 @@ It is just a matter of appropriately relabeling the variables in Hall's model.
 In this lecture, we'll
 
 * show how the solution to the LQ permanent income model can be obtained using LQ control methods
-* represent the model as a linear state space system as in {doc}`this lecture <../tools_and_techniques/linear_models>`
+* represent the model as a linear state space system as in {doc}`this lecture <../introduction_dynamics/linear_models>`
 * apply [QuantEcon](http://quantecon.org/quantecon-jl)'s [LSS](https://github.com/QuantEcon/QuantEcon.jl/blob/master/src/lss.jl) type to characterize statistical features of the consumer's optimal consumption and borrowing plans
 
 We'll then use these characterizations to construct a simple model of cross-section wealth and
@@ -159,7 +159,7 @@ y_{t+1} = \alpha + \rho_1 y_t + \rho_2 y_{t-1} + \sigma w_{t+1}
 $$
 
 We can map this into the linear state space framework in {eq}`sprob15ab2`, as
-discussed in our lecture on  {doc}`linear models <../tools_and_techniques/linear_models>`.
+discussed in our lecture on  {doc}`linear models <../introduction_dynamics/linear_models>`.
 
 To do so we take