### A Pluto.jl notebook ###
# v0.20.4
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
#! format: off
quote
local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end
local el = $(esc(element))
global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)
el
end
#! format: on
end
# ╔═╡ d257d214-0e18-444d-aad9-5f37ec978cf5
begin
using Plots
using Interpolations
using FastGaussQuadrature
end
# ╔═╡ d40c12c8-b0ac-4c2c-ad5c-86c918aad387
using PlutoUI
# ╔═╡ f56ca31c-763d-4908-b48c-d477e6fa8e3a
html"""
"""
# ╔═╡ 2cc56380-3907-4be6-b699-288093fccb50
begin
struct TwoColumn{L, R}
left::L
right::R
end
function Base.show(io, mime::MIME"text/html", tc::TwoColumn)
write(io, """
""")
show(io, mime, tc.left)
write(io, """
""")
show(io, mime, tc.right)
write(io, """
""")
end
end
# ╔═╡ b581ffce-9d63-4fbf-992b-8f9280d83a92
html""
# ╔═╡ 5285eb0a-a266-11eb-277e-a7c01cf568df
md"
# Endogenous Grid Method after Chris Caroll (EconLetters 2006)
**With and Without Discrete Choices**
*Carlo Alberto 2025, Florian Oswald*
"
# ╔═╡ 32579cd1-07c0-43f5-bde6-5a6a9f52a999
md"
#
* We have encountered 2 methods to solve the consumption savings problem in class:
1. VFI (value function iteration)
2. PFI (policy function iteration)
* We saw that PFI works better in our applications.
* Still, we needed to numerically solve for the root of a nonlinear equation (the Euler Euqation), and that is expensive.
* Let's introduce EGM now by way of example. EGM avoids *all* numerical root finding operations!
* At the end of the lecture we will introduce *discrete* choices into the consumption-savings world and learn about the DC-EGM extension to Carroll's method
"
# ╔═╡ 599b4b2e-dcfc-4790-a0bd-f9a60e9fadd1
md"
## Consumption-Savings Model (after Deaton)
$$V(M)=\max_{0 \le c \le M}\big\{u(c)+\beta \mathbb{E}_{y} V\big(\underset{=M'}{\underbrace{R(M-c)+y}}\big)\big\}$$
Here, we introduced a new state variable $M$ which denotes *cash-on-hand*, or, *all consumable resources*. Typically, we would lump together income and liquid assets in $M$ (like how much is on your bank account and how big is your income together determines the size of $M$)
- discrete time, infinite horizon
- one continuous choice of consumption $0 \le c \le M$
- state space: consumable resources in the beginning of the period $ M $, discretized
- Cash on hand evolves as $M' = R(M-c)+y$
- income $y$ is log-normal distribution with $\mu = 0$ and $\sigma > 0$
"
# ╔═╡ 0171a47c-f777-410d-9814-8c86da85bf3e
md"
## Euler Equation Solver for Deaton
* The FOC for this model is
$$u'(c^\star) - \beta R \mathbb{E}_{y} V'\big(R(M-c^\star)+y\big) = 0$$
where $c^\star$ denotes *optimal consumption*
* Next, we remember the Envelope Condition, differentiating the value function by it's state variable $M$:
## Envelope Theorem in Deaton Model
Let us revisit the *envelope theorem* for this model class. First, define an implicit function $F$ of state $M$ and choice $c$ as follows:
$$F(M,c)=u(c)+\beta \mathbb{E}_{y} V\big(\underset{=M'}{\underbrace{R(M-c)+y}}\big)$$
so that the policy function $c^\star(M)$ satisfies $V(M)=F(M,c^\star(M))$. Next, we want to compute the derivative of the 2-argument function $F$ wrt its argument $M$ in each slot. We need the *chain rule*!
$$\begin{align}
\frac{d V(M)}{dM} &= \frac{d F(M,c^\star(M))}{dM}\\
&= \tfrac{\partial F(M,c^\star)}{\partial M} + \underset{=0\text{ by FOC}}{\underbrace{\tfrac{\partial F(M,c^\star)}{\partial c^\star}}} \tfrac{\partial c^\star(M)}{\partial M}
= \tfrac{\partial F(M,c^\star)}{\partial M} = \beta R \mathbb{E}_{y} V'\big(R(M-c^\star)+y\big)
\end{align}$$
In short, the envelope condition states:
$$V'(M) = \beta R \mathbb{E}_{y} V'\big(R(M-c^\star)+y\big)$$
which means that we have
$$u'(c^\star) = V'(M)$$
in each period.
## Building up the Euler Equation
1. FOC:
$$u'(c^\star) - \beta R \mathbb{E}_{y} V'\big(R(M-c^\star)+y\big) = 0$$
2. Envelope:
$$V'(M) = \beta R \mathbb{E}_{y} V'\big(R(M-c^\star)+y\big), \forall t$$
3. Combine
$$u'\big(c^\star(M)\big) = \beta R \mathbb{E}_{y} u'\big(c^\star\big(\underset{=M'}{\underbrace{R[M-c^\star(M)]+y}}\big)\big)$$
"
# ╔═╡ bf784923-f460-4816-87d6-5890f959e015
md"
## A New Variable: End-of-period Assets $A$
* Let's introduce a new variable: the *post-decision state variable*. Here: how many assets left at end of period *after* consumption took place?
* How much do you *save* today (and before you earned gross interest $R$ on it, at the start of next period)?
* Let us denote this $A$. It is useful to think throught the timing of the model:
$$M \rightarrow c(M) \rightarrow A = M-c(M) \rightarrow M' = R(M-c(M)) + y = RA + y$$
* The constraint is that we cannot consume more than we have consumable resources, which bounds $A \in [0, M]$
$$0 \le c \le M \; \Rightarrow \; 0 \le A = M-c \le M$$
"
# ╔═╡ 2dd54158-d62f-4b12-9c18-486e0a52596a
md"
## Euler Equation in terms of $A$
* Let's replace $M - c(M) = A$ in the Euler Euqation.
$$u'\big(c(M)\big) = \beta R \mathbb{E}_{y} u'\big(c(RA+\tilde{y})\big)$$
* Any optimal policy function $c^\star$ will satisfy this equation for $A = M- c(M)$ (in which case it is *just* our Euler Equation from before).
* So, *again* we will want to find the function $c^\star$, as in PFI. How?
* Given an current candidate function $c$, the next update $c^\dagger$ will be derived by taking the inverse of the marginal utility function on both sides of the above equation:
$$\begin{cases}
c^\dagger = (u')^{-1} \Big( \beta R \mathbb{E}_{y} u'(c\big(RA+y)\big) \Big) \\
M^\dagger = A + c^\dagger
\end{cases}$$
* A key insight is that the new set of points in $M^\dagger$ will be *endogenously determined*, hence the name of the method: Given $A$, the structure of the model (embedded in the euler equation) will imply optimal consumption *and* a set of corresponding grid points for cash on hand.
"
# ╔═╡ 51b82e35-fba3-4100-b313-254ece33449d
md"
## Standard PFI vs EGM PFI
"
# ╔═╡ eb1b201c-1598-4740-b7e9-5108f017f4e3
TwoColumn(
md"
### Standard
* We search for function $c^{\dagger}(M)$ that solves
$$u'\big(c^{\dagger}(M)\big) = \beta R \mathbb{E}_{y} u'\big(c[R(M-c^{\dagger}(M))+y]\big)$$
1. We fix a grid over $M$, say, $\mathcal{M} = \{m_1, m_2,\dots,m_n\}$
2. For each $m_i \in \mathcal{M}$, solve the above equation to obtain $c^{\dagger}(m_i)$
👉 must numerically solve a nonlinear equation with a root finder $n$ times.
",
md"
### EGM
1. Fix a grid over $A$.
2. Given current guess of policy function $c(M)$, (and given current *grid* $M$!), **directly** compute the next iteration of the policy function (and the **new** grid $M^\dagger$) as follows.
3. Iterating over all points $A_j \in A$,
$$\begin{cases}
c_j = (u')^{-1} \Big( \beta R \mathbb{E}_{y} u'(c\big(RA_j+y)\big) \Big) \\
M_j = A_j + c_j
\end{cases}$$
👉 The next policy function $c^\dagger$ is the interpolation of points, i.e. $c^\dagger(M^\dagger) \equiv \{(M_i, c_i)\}_{i=1}^n$
👉 no numerical root solving or other optimization needed!
")
# ╔═╡ 2333dde1-68cc-4155-82e7-d8727ef596f0
md"
## The EGM Step
Let's put down that algorithm again:
* Given a grid point $A_j \in A$,
1. Compute all tomorrow's potential cash on hands, filling in integration nodes for $y_k$: $M' = RA_j + y_k$
2. Get optimal consumption at all those potential cash on hand levels, using the current guess of $c$.
3. Compute marginal utility of each consumption and hence complete the RHS of the Euler equation
4. Take inverse of marginal utility function to recover current period $c_j$ corresponding to end of period $A_j$
5. Finish EGM step by accounting identity $M_j = A_j + c_j$
"
# ╔═╡ 590b81d4-26bc-46c8-82ed-2998fcffe706
md"
## Simple EGM Implementation
1. Log utility
1. fixed income (no integration): $y = \bar{y}$
$$V(M)=\max_{0 \le c \le M}\big\{\log(c)+\beta V\big(R(M-c)+y\big)\big\}$$
$$u'\big(c^\star(M)\big) = \beta R u'\big(c^\star\big(R[M-c^\star(M)]+y\big)\big)$$
EGM-step:
$$\begin{cases}
c^\dagger = (u')^{-1} \Big( \beta R u'(c\big(RA+y)\big) \Big) \\
M^\dagger = A + c^\dagger
\end{cases}$$
"
# ╔═╡ e6376b86-56ee-4ace-9e9a-5ceb87bed7db
begin
n = 5 # num points
M = 10 # max cash on hand
y = 0 # income: zero
R = 1.05 # gross interest
β = 0.95
T = 5 # let's do 5 iterations
agrid = range(0.0,stop = M,length = n)
c = Vector{Float64}[Float64[] for i in 1:T] # consumption function on m
m = Vector{Float64}[Float64[] for i in 1:T] # endo grids
# start from known last period
# (or view as some arbitrary starting value)
m[T] = [0.0,M]
c[T] = [0.0,M]
u(c) = log(c) # utility
mu(c) = 1/c # marginal utility
imu(u) = 1/u # inverse of marginal utility
end
# ╔═╡ 8292ee9b-e13a-4921-888f-4f898cebc6c4
function det_egm_single(aj,policy,y)
mplus = max(R * aj + y, 1e-10) # all levels of next cash on hand
cplus = policy(mplus)
c = imu( β * R * mu( cplus ) ) # RHS of Euler Equation
m = aj + c
(m,c)
end
# ╔═╡ 0eb5052b-7d30-48d4-926e-15a43c3ee7de
md"
## First Iteration
"
# ╔═╡ 48b0b0dd-9487-4fbd-8ec7-bf61272d4ba6
function makeanim()
# current policy
polT = extrapolate(interpolate((m[T],), c[T], Gridded(Linear())), Linear())
pltT = plot(m[T], c[T],title = "First Iteration: T-1", xlim = (-1,22), ylim = (-1,20),color = :black, marker = :circle, label = "period T", xlab = "M", ylab = "c(M)", ratio = 1)
anim = @animate for j in eachindex(agrid)
mj,cj = det_egm_single(agrid[j],polT,y)
scatter!(pltT,[mj],[cj], label = "A$j = $(round(agrid[j],digits=1))")
end
anim
end
# ╔═╡ 3317d300-0e2d-481b-ab0c-7c1fcd319162
TwoColumn(
md"""
For each $i = 1,\dots,5$, let's take end of period assets $A_i$ and feed it through the EGM step:
$$\begin{cases}
c_i^\dagger = (u')^{-1} \Big( \beta R u'(c\big(R \cdot A_i+y)\big) \Big) \\
M_i^\dagger = A_i + c_i^\dagger
\end{cases}$$
Notice that $M^\dagger$ is *not* on the $A$-grid!
""",
gif(makeanim(), fps = 0.7)
)
# ╔═╡ 5ddaef03-bbdf-4374-82e7-fc9eb3d8fd12
function det_EGM(m0,c0,n,y,agrid)
policy = extrapolate(interpolate((m0,), c0, Gridded(Linear())), Linear())
m1 = zeros(n+1) # n+1 because add 0 in first slot for borrowing constraint
c1 = zeros(n+1)
for (aj,a) in enumerate(agrid)
m1[aj+1], c1[aj+1] = det_egm_single(a,policy,y)
end
m1,c1
end
# ╔═╡ fa84b929-6088-4bba-bb29-d0cc4d8f9b9d
md"
#
"
# ╔═╡ 034ff9fe-b925-4f6c-b9f8-e28a86e7ef9d
m[T-1], c[T-1] = det_EGM(m[T],c[T],n,y,agrid)
# ╔═╡ c12a930f-cf42-46e8-9f88-06d00aaba002
p = plot(m[T], c[T],title = "EGM Iterations", xlim = (-1,22), ylim = (-1,20),color = :black, marker = :circle, label = "period T", xlab = "M", ylab = "c(M)")
# ╔═╡ aac8dfa3-97b1-4786-baea-8f85524a27ac
md"
#
"
# ╔═╡ 6616459c-4542-4e5d-9f1f-926ebff695d2
plot!(p, m[T-1], c[T-1], color = :green, label = "T-1", marker = :circle)
# ╔═╡ 3f2351e8-aecc-420f-ac3c-f5322021a359
md"
#
"
# ╔═╡ 4aba99ee-455d-44b8-8630-be8a391ac291
m[T-2], c[T-2] = det_EGM(m[T-1],c[T-1],n,y,agrid)
# ╔═╡ 8cdbb45a-e735-4f94-97d7-a4db0fc5ec9b
plot!(p, m[T-2], c[T-2], color = :yellow, label = "T-2", marker = :circle)
# ╔═╡ 86b13890-69de-4c8f-887a-1b016d33bb43
md"
#
"
# ╔═╡ f01fef94-8a3e-4851-8be3-59c25f404a5c
m[T-3], c[T-3] = det_EGM(m[T-2],c[T-2],n,y,agrid)
# ╔═╡ 30c57fee-74dd-4e8d-a4f7-33794588c1e1
plot!(p, m[T-3], c[T-3], color = :orange, label = "T-3", marker = :circle)
# ╔═╡ 82b08239-9522-4403-a32a-5ebefd57f18e
md"
#
"
# ╔═╡ 6a20c4d3-c392-4e04-8cfd-332f400424ac
for it in (T-1):-1:1
m[it],c[it] = det_EGM(m[it+1],c[it+1],n,y,agrid)
end
# ╔═╡ 284fed7e-7585-48dc-90fc-c00835863735
begin
p1 = plot(m[T], c[T],title = "EGM Iterations", xlim = (-1,22), ylim = (-1,20),color = :black, marker = :circle, label = "period 5", xlab = "M", ylab = "c(M)")
for it in (T-1):-1:1
plot!(p1,m[it],c[it], label = "period $it",marker = :circle)
end
end
# ╔═╡ 69a832cc-766c-42cb-be9f-68e8b89f01cb
p1
# ╔═╡ 58656e5c-5356-4d86-ad7b-33745623e096
md"
## Corner Solutions
* So far this only covers interior solutions where the Euler Equation holds exactly.
* The choice of $c$ is contrained to lie in $[0,M]$, however, which means that $0\leq A \leq M$
* The method *only works with* points $A$ chosen to respect that constraint, so there is never any issue.
* But we need to take care of the bounds ourselves.
### Lower Bound on Consumption
* Standard Inada conditions $\lim_{c\to0} = -\infty$ prevents us from ever hitting $c=0$
"
# ╔═╡ 80668dc3-ca1d-4dfb-b68e-ad64ae7abebc
md"
#
"
# ╔═╡ 8bb5d76f-cddc-45db-a0ce-1cc9eca1bc94
md"
### Upper Bound
* If we consume all resources at the upper bound, $c = M$, and we can compute the corresponding solution directly. Note that $A=0$ at that point (we save zero).
* We can rely on the fact that in this class of models with a monotonically increasing utilty function, $A = M-c$ is non-decreasing in $M$.
* The final point $M_0$ corresponding to $A=0$ is
$$M_0 = c_0 + 0 = (u')^{-1} \Big( \beta R \mathbb{E}_{y} u'(\tilde{c}\big) \Big) + 0$$
* Given non-decreasing property, for all $M < M_0$, we must have $A=0$.
* That implies that we consume all resources, hence $c = M$ at such points.
* This is just a 45 degree line connecting point $(0,0)$ and $M_0$.
"
# ╔═╡ cd05846a-0030-44e9-a4b3-01f141f8bfc2
md"
#
"
# ╔═╡ 3dbb96da-0caa-4c16-b9cb-44e447a96b22
begin
c2 = Vector{Float64}[Float64[] for i in 1:T] # consumption function on m
m2 = Vector{Float64}[Float64[] for i in 1:T] # endo grids
m2[T] = [0.0,M]
c2[T] = [0.0,M]
p2 = plot(m[T], c[T],title = "EGM Iterations with Corner", xlim = (-0.2,5), ylim = (-0.2,5),color = :black, marker = :circle, label = "period 5", xlab = "M", ylab = "c(M)")
for it in (T-1):-1:1
m2[it],c2[it] = det_EGM(m[it+1],c[it+1],n,1.0,agrid)
plot!(p2,m2[it],c2[it], label = "period $it")
end
p2
end
# ╔═╡ 845f261e-9865-4cb3-a772-a46f0251f67e
md"
## Challenge
* Taking the functions from above, build an EGM solver for an infinite time model!
* Stop your iteration once successive policy funciton approximation do not incur a greater maximal absolute error than 1e-6
* write a function with the following keyword arguments: `n = 100, M = 10, tol = 1e-6,y = 1.0`
* Make a plot of the final policy function!
"
# ╔═╡ 1fefbafe-8fa9-4be3-a219-922dcf758e0e
md"
#
"
# ╔═╡ abf95185-5948-496f-9058-369587c19a1b
function EGM_inf(;n = 100, M = 10, tol = 1e-6,y = 1.0)
agrid = range(0.0,stop = M, length = n)
c = zeros(n+1)
m = zeros(n+1)
c0 = collect(range(0.0,stop = M, length = n+1))
m0 = collect(range(0.0,stop = M, length = n+1))
err = 100.0
iters = 0
while err > tol
m, c = det_EGM(m0, c0,n,y,agrid)
err = max( maximum(abs.(c .- c0)), maximum(abs.(m .- m0)) )
# update from current to new functions
m0[:] = m
c0[:] = c
iters += 1
end
(m,c,iters)
end
# ╔═╡ 00a5a487-4bf4-44bc-938a-e794c7d7ac6d
begin
im,ic,iters = EGM_inf()
plot(EGM_inf(),ylims = (0,2),leg = false, title = "Infinite Time EGM converged after $iters steps")
end
# ╔═╡ 089d57dd-fdbf-454f-9263-3e2ca155346e
md"
# Minimal EGM with uncertainty
* Let's go back to the initial example with log-normal income $y$
* We need to actually integrate over the RHS of the Euler Equation now.
* Reminder: $y \sim \text{LogNormal}(\mu,\sigma) \iff y = \exp(X), X \sim N(\mu,\sigma)$
* So, a good approximation scheme for the log-normal distribution can be based on getting nodes and weights for $X \sim N(\mu,\sigma)$, and then using $y_i = \exp(x_i)$, where $x_i$ is gauss-hermite node, will work well.
#
"
# ╔═╡ e80e1f7c-aa7f-48c3-a151-57ceea02286b
function minimal_EGM(;ny=5,na=100,nT=25,M=10,σ=0.25,μ=0.0,R=1.05,β=0.95,cbar = 0.0)
nodes,weights = gausshermite(ny) # from FastGaussQuadrature
yvec = sqrt(2.0) * σ .* nodes .+ μ # gauss-hermite nodes
ywgt = weights .* π^(-0.5)
avec = collect(range(0.0,M,length = na))
m = Vector{Float64}[Float64[] for i in 1:nT] # endogenous grid
c = Vector{Float64}[Float64[] for i in 1:nT] # consumption function on m
m[nT] = [0.0,M]
c[nT] = [cbar,M]
cg = cgrad(:viridis)
cols = cg[range(0.0,stop=1.0,length=nT)]
pl = plot(m[nT],c[nT],label="$(nT)",leg=:topright,title="Consumption Function",
xlims = (0,M),ylims = (0,M), color = cols[nT],
xlab = "Cash on Hand", ylab = "Consumption")
# cycle back in time
for it in nT-1:-1:1
# w1 = yshock*R*savings: next period wealth at all states. (ny,na)
w1 = exp.(yvec) .+ R .* avec'
# get next period consumption on that wealth w1
policy = interpolate((m[it+1],),c[it+1],Gridded(Linear()))
policy = extrapolate(policy,Line())
c1 = reshape(policy(w1[:]),ny,na)
c1[c1 .< cbar] .= 0.001 # don't allow negative consumption
Emu = ywgt' * (1 ./ c1) # Expected marginal utility (na,1)
rhs = β * R * Emu[:] # RHS of euler equation
c[it] = 1.0 ./ rhs # imu(u) -> c
m[it] = avec .+ c[it] # c -> M
# add credit constraint region
c[it] = vcat(cbar, c[it]) # prepend with 0
m[it] = vcat(0.0, m[it]) #
plot!(pl,m[it],c[it],label= it == 1 ? "$it" : "", color = cols[it])
end
pl = lens!(pl, [0, 2], [0, 2], inset = (1, bbox(0.2, 0.1, 0.25, 0.25)))
pl
end
# ╔═╡ c7dde73c-e626-404d-9177-f58fc1df3972
@bind σ Slider(0.01:0.1:2.0,show_value = true)
# ╔═╡ b03ff849-715d-47ee-9299-782c14636c01
@bind μ Slider(0.0:0.1:2.0,show_value = true)
# ╔═╡ 2203856e-87fe-4a29-90c7-e0b4a065c4a1
@bind r Slider(1.0:0.01:1.1,show_value = true)
# ╔═╡ 8cd2061e-dd49-4521-94bf-de6e732af151
@bind cbar Slider(0.0:0.01:1.1,show_value = true)
# ╔═╡ b9c90c2c-4c1c-40d4-974b-e148191f67ee
minimal_EGM(σ=σ, μ = μ, R=r, cbar = cbar)
# ╔═╡ 6c7e3163-06e4-4ad6-8d55-53f8b596bde8
md"
# Conclusions about EGM
1. Very fast (not demonstrated) and accurate method
2. Works for finite and inifinite time horizon models with 1 continuous choice variable and 1 continuous state variable
3. Need analytic utility function that is invertible
4. Need to be able to compute a post-decision state variable
5. Can handle occasionally binding borrowing constraints
This is a small class of models, but very important in macro. If your application has the above features, you should almost certainly use EGM to compute the solution.
"
# ╔═╡ b62d9921-157b-4143-8246-ddd8163e6e4a
md"
# Further Resources
1. Chris Caroll's [original article](http://www.econ2.jhu.edu/people/ccarroll/EndogenousGridpoints.pdf)
1. Barillas & Fernandez-Villaverde, JEDC 2007 “A Generalization of the Endogenous Grid Method”
1. Ludwig & Schön, Computational Economics, 2018 “Endogenous Grids in Higher Dimensions: Delaunay Interpolation and Hybrid Methods”
1. Matthew White, JEDC 2015 “The Method of Endogenous Gridpoints in Theory and Practice”
1. Iskhakov, Econ Letters 2015 “Multidimensional endogenous gridpoint method: solving triangular dynamic stochastic optimization problems without root-finding operations” + Corrigendum
## Generalizations
* Adding a discrete choice makes a non-convex problem out of this: Euler Equation is no longer sufficient.
1. Iskhakov, Jørgensen, Rust, Schjerning, QE 2017 “The Endogenous Grid Method for Discrete-Continuous Dynamic Choice Models with (or without) Taste Shocks”
2. Giulio Fella, RED 2014 “A Generalized Endogenous Grid Method for Non-Smooth and Non-Concave Problems”
3. Jeppe Druedahl, Thomas Jørgensen, JEDC 2017 “A General Endogenous Grid Method for Multi-Dimensional Models with Non-Convexities and Constraints”
## DC-EGM
* Let's look at [Fedor's slides](https://github.com/dseconf/DSE2019/blob/master/11_DCEGM_Iskhakov/slides/dcegm_DSE2019.pdf) to learn more about this algorithm
* Then we will look at [my implementation of it on github](https://github.com/floswald/DCEGM.jl)
"
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SparseArraysExt = ["SparseArrays"]
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deps = ["Artifacts", "Libdl", "libblastrampoline_jll"]
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deps = ["Dates"]
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ConstructionBaseUnitfulExt = "ConstructionBase"
InverseFunctionsUnitfulExt = "InverseFunctions"
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ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112"
[[deps.UnitfulLatexify]]
deps = ["LaTeXStrings", "Latexify", "Unitful"]
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deps = ["Artifacts", "JLLWrappers", "Libdl", "Wayland_jll", "Xorg_libX11_jll", "Xorg_libXrandr_jll", "xkbcommon_jll"]
git-tree-sha1 = "2f0486047a07670caad3a81a075d2e518acc5c59"
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[[deps.Wayland_protocols_jll]]
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git-tree-sha1 = "93f43ab61b16ddfb2fd3bb13b3ce241cafb0e6c9"
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[[deps.WoodburyMatrices]]
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[[deps.XML2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Zlib_jll"]
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[[deps.XSLT_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "XML2_jll", "Zlib_jll"]
git-tree-sha1 = "a54ee957f4c86b526460a720dbc882fa5edcbefc"
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deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "15e637a697345f6743674f1322beefbc5dcd5cfc"
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[[deps.Xorg_libICE_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "326b4fea307b0b39892b3e85fa451692eda8d46c"
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[[deps.Xorg_libSM_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libICE_jll"]
git-tree-sha1 = "3796722887072218eabafb494a13c963209754ce"
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[[deps.Xorg_libX11_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
git-tree-sha1 = "afead5aba5aa507ad5a3bf01f58f82c8d1403495"
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git-tree-sha1 = "6035850dcc70518ca32f012e46015b9beeda49d8"
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[[deps.Xorg_libXcursor_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXfixes_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "12e0eb3bc634fa2080c1c37fccf56f7c22989afd"
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[[deps.Xorg_libXdmcp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "34d526d318358a859d7de23da945578e8e8727b7"
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[[deps.Xorg_libXext_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libX11_jll"]
git-tree-sha1 = "d2d1a5c49fae4ba39983f63de6afcbea47194e85"
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git-tree-sha1 = "0e0dc7431e7a0587559f9294aeec269471c991a4"
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[[deps.Xorg_libXi_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXfixes_jll"]
git-tree-sha1 = "89b52bc2160aadc84d707093930ef0bffa641246"
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version = "1.7.10+4"
[[deps.Xorg_libXinerama_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll"]
git-tree-sha1 = "26be8b1c342929259317d8b9f7b53bf2bb73b123"
uuid = "d1454406-59df-5ea1-beac-c340f2130bc3"
version = "1.1.4+4"
[[deps.Xorg_libXrandr_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "34cea83cb726fb58f325887bf0612c6b3fb17631"
uuid = "ec84b674-ba8e-5d96-8ba1-2a689ba10484"
version = "1.5.2+4"
[[deps.Xorg_libXrender_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libX11_jll"]
git-tree-sha1 = "47e45cd78224c53109495b3e324df0c37bb61fbe"
uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa"
version = "0.9.11+0"
[[deps.Xorg_libpthread_stubs_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "8fdda4c692503d44d04a0603d9ac0982054635f9"
uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74"
version = "0.1.1+0"
[[deps.Xorg_libxcb_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
git-tree-sha1 = "bcd466676fef0878338c61e655629fa7bbc69d8e"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.17.0+0"
[[deps.Xorg_libxkbfile_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libX11_jll"]
git-tree-sha1 = "730eeca102434283c50ccf7d1ecdadf521a765a4"
uuid = "cc61e674-0454-545c-8b26-ed2c68acab7a"
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[[deps.Xorg_xcb_util_cursor_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_xcb_util_image_jll", "Xorg_xcb_util_jll", "Xorg_xcb_util_renderutil_jll"]
git-tree-sha1 = "04341cb870f29dcd5e39055f895c39d016e18ccd"
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[[deps.Xorg_xcb_util_image_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "0fab0a40349ba1cba2c1da699243396ff8e94b97"
uuid = "12413925-8142-5f55-bb0e-6d7ca50bb09b"
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[[deps.Xorg_xcb_util_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll"]
git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1"
uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5"
version = "0.4.0+1"
[[deps.Xorg_xcb_util_keysyms_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00"
uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7"
version = "0.4.0+1"
[[deps.Xorg_xcb_util_renderutil_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e"
uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e"
version = "0.3.9+1"
[[deps.Xorg_xcb_util_wm_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67"
uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361"
version = "0.4.1+1"
[[deps.Xorg_xkbcomp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libxkbfile_jll"]
git-tree-sha1 = "330f955bc41bb8f5270a369c473fc4a5a4e4d3cb"
uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4"
version = "1.4.6+0"
[[deps.Xorg_xkeyboard_config_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_xkbcomp_jll"]
git-tree-sha1 = "691634e5453ad362044e2ad653e79f3ee3bb98c3"
uuid = "33bec58e-1273-512f-9401-5d533626f822"
version = "2.39.0+0"
[[deps.Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "e92a1a012a10506618f10b7047e478403a046c77"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
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[[deps.Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
version = "1.2.13+1"
[[deps.Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "555d1076590a6cc2fdee2ef1469451f872d8b41b"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.5.6+1"
[[deps.eudev_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "gperf_jll"]
git-tree-sha1 = "431b678a28ebb559d224c0b6b6d01afce87c51ba"
uuid = "35ca27e7-8b34-5b7f-bca9-bdc33f59eb06"
version = "3.2.9+0"
[[deps.fzf_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "936081b536ae4aa65415d869287d43ef3cb576b2"
uuid = "214eeab7-80f7-51ab-84ad-2988db7cef09"
version = "0.53.0+0"
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deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "3516a5630f741c9eecb3720b1ec9d8edc3ecc033"
uuid = "1a1c6b14-54f6-533d-8383-74cd7377aa70"
version = "3.1.1+0"
[[deps.libaom_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "1827acba325fdcdf1d2647fc8d5301dd9ba43a9d"
uuid = "a4ae2306-e953-59d6-aa16-d00cac43593b"
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[[deps.libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Zlib_jll"]
git-tree-sha1 = "e17c115d55c5fbb7e52ebedb427a0dca79d4484e"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.15.2+0"
[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
version = "5.11.0+0"
[[deps.libdecor_jll]]
deps = ["Artifacts", "Dbus_jll", "JLLWrappers", "Libdl", "Libglvnd_jll", "Pango_jll", "Wayland_jll", "xkbcommon_jll"]
git-tree-sha1 = "9bf7903af251d2050b467f76bdbe57ce541f7f4f"
uuid = "1183f4f0-6f2a-5f1a-908b-139f9cdfea6f"
version = "0.2.2+0"
[[deps.libevdev_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "141fe65dc3efabb0b1d5ba74e91f6ad26f84cc22"
uuid = "2db6ffa8-e38f-5e21-84af-90c45d0032cc"
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deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "8a22cf860a7d27e4f3498a0fe0811a7957badb38"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "2.0.3+0"
[[deps.libinput_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "eudev_jll", "libevdev_jll", "mtdev_jll"]
git-tree-sha1 = "ad50e5b90f222cfe78aa3d5183a20a12de1322ce"
uuid = "36db933b-70db-51c0-b978-0f229ee0e533"
version = "1.18.0+0"
[[deps.libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Zlib_jll"]
git-tree-sha1 = "b70c870239dc3d7bc094eb2d6be9b73d27bef280"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.44+0"
[[deps.libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "490376214c4721cdaca654041f635213c6165cb3"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.7+2"
[[deps.mtdev_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "814e154bdb7be91d78b6802843f76b6ece642f11"
uuid = "009596ad-96f7-51b1-9f1b-5ce2d5e8a71e"
version = "1.1.6+0"
[[deps.nghttp2_jll]]
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uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
version = "1.59.0+0"
[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
version = "17.4.0+2"
[[deps.x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "2021.5.5+0"
[[deps.x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.5.0+0"
[[deps.xkbcommon_jll]]
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git-tree-sha1 = "9c304562909ab2bab0262639bd4f444d7bc2be37"
uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
version = "1.4.1+1"
"""
# ╔═╡ Cell order:
# ╟─f56ca31c-763d-4908-b48c-d477e6fa8e3a
# ╟─d257d214-0e18-444d-aad9-5f37ec978cf5
# ╟─2cc56380-3907-4be6-b699-288093fccb50
# ╟─b581ffce-9d63-4fbf-992b-8f9280d83a92
# ╟─5285eb0a-a266-11eb-277e-a7c01cf568df
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