We solved the stochastic optimal growth model using
We found time iteration to be significantly more accurate at each step
In this lecture we’ll look at an ingenious twist on the time iteration technique called the endogenous grid method (EGM)
EGM is a numerical method for implementing policy iteration invented by Chris Carroll
It is a good example of how a clever algorithm can save a massive amount of computer time
(Massive when we multiply saved CPU cycles on each implementation times the number of implementations worldwide)
The original reference is [Car06]
Let’s start by reminding ourselves of the theory and then see how the numerics fit in
Take the model set out in the time iteration lecture, following the same terminology and notation
The Euler equation is
$$ (u'\circ c^*)(y) = \beta \int (u'\circ c^*)(f(y - c^*(y)) z) f'(y - c^*(y)) z \phi(dz) \tag{1} $$
As we saw, the Coleman operator is a nonlinear operator $ K $ engineered so that $ c^* $ is a fixed point of $ K $
It takes as its argument a continuous strictly increasing consumption policy $ g \in \Sigma $
It returns a new function $ Kg $, where $ (Kg)(y) $ is the $ c \in (0, \infty) $ that solves
$$ u'(c) = \beta \int (u' \circ g) (f(y - c) z ) f'(y - c) z \phi(dz) \tag{2} $$
As discussed in the lecture on time iteration, to implement the method on a computer we need numerical approximation
In particular, we represent a policy function by a set of values on a finite grid
The function itself is reconstructed from this representation when necessary, using interpolation or some other method
Previously, to obtain a finite representation of an updated consumption policy we
Each $ c_i $ is then interpreted as the value of the function $ K g $ at $ y_i $
Thus, with the points $ \{y_i, c_i\} $ in hand, we can reconstruct $ Kg $ via approximation
Iteration then continues…
The method discussed above requires a root finding routine to find the $ c_i $ corresponding to a given income value $ y_i $
Root finding is costly because it typically involves a significant number of function evaluations
As pointed out by Carroll [Car06], we can avoid this if $ y_i $ is chosen endogenously
The only assumption required is that $ u' $ is invertible on $ (0, \infty) $
The idea is this:
First we fix an exogenous grid $ \{k_i\} $ for capital ($ k = y - c $)
Then we obtain $ c_i $ via
$$ c_i = (u')^{-1} \left\{ \beta \int (u' \circ g) (f(k_i) z ) \, f'(k_i) \, z \, \phi(dz) \right\} \tag{3} $$
where $ (u')^{-1} $ is the inverse function of $ u' $
Finally, for each $ c_i $ we set $ y_i = c_i + k_i $
It is clear that each $ (y_i, c_i) $ pair constructed in this manner satisfies (2)
With the points $ \{y_i, c_i\} $ in hand, we can reconstruct $ Kg $ via approximation as before
The name EGM comes from the fact that the grid $ \{y_i\} $ is determined endogenously
Let’s implement this version of the Coleman operator and see how it performs
Here’s an implementation of $ K $ using EGM as described above
using InstantiateFromURL
github_project("QuantEcon/quantecon-notebooks-julia", version = "0.1.0")
using LinearAlgebra, Statistics, Compat
using BenchmarkTools, Interpolations, Parameters, Plots, QuantEcon, Random, Roots
gr(fmt = :png);
function coleman_egm(g, k_grid, β, u′, u′_inv, f, f′, shocks)
# Allocate memory for value of consumption on endogenous grid points
c = similar(k_grid)
# Solve for updated consumption value
for (i, k) in enumerate(k_grid)
vals = u′.(g.(f(k) * shocks)) .* f′(k) .* shocks
c[i] = u′_inv(β * mean(vals))
end
# Determine endogenous grid
y = k_grid + c # y_i = k_i + c_i
# Update policy function and return
Kg = LinearInterpolation(y,c, extrapolation_bc=Line())
Kg_f(x) = Kg(x)
return Kg_f
end
Note the lack of any root finding algorithm
We’ll also run our original implementation, which uses an exogenous grid and requires root finding, so we can perform some comparisons
function K!(Kg, g, grid, β, u′, f, f′, shocks)
# This function requires the container of the output value as argument Kg
# Construct linear interpolation object #
g_func = LinearInterpolation(grid, g, extrapolation_bc = Line())
# solve for updated consumption value #
for (i, y) in enumerate(grid)
function h(c)
vals = u′.(g_func.(f(y - c) * shocks)) .* f′(y - c) .* shocks
return u′(c) - β * mean(vals)
end
Kg[i] = find_zero(h, (1e-10, y - 1e-10))
end
return Kg
end
# The following function does NOT require the container of the output value as argument
K(g, grid, β, u′, f, f′, shocks) =
K!(similar(g), g, grid, β, u′, f, f′, shocks)
Let’s test out the code above on some example parameterizations, after the following imports
As we did for value function iteration and time iteration, let’s start by testing our method with the log-linear benchmark
The first step is to bring in the model that we used in the Coleman policy function iteration
# model
Model = @with_kw (α = 0.65, # productivity parameter
β = 0.95, # discount factor
γ = 1.0, # risk aversion
μ = 0.0, # lognorm(μ, σ)
s = 0.1, # lognorm(μ, σ)
grid_min = 1e-6, # smallest grid point
grid_max = 4.0, # largest grid point
grid_size = 200, # grid size
u = γ == 1 ? log : c->(c^(1-γ)-1)/(1-γ), # utility function
u′ = c-> c^(-γ), # u'
f = k-> k^α, # production function
f′ = k -> α*k^(α-1), # f'
grid = range(grid_min, grid_max, length = grid_size)) # grid
Next we generate an instance
mlog = Model(); # Log Linear model
We also need some shock draws for Monte Carlo integration
Random.seed!(42); # For reproducible behavior.
shock_size = 250 # Number of shock draws in Monte Carlo integral
shocks = exp.(mlog.μ .+ mlog.s * randn(shock_size));
As a preliminary test, let’s see if $ K c^* = c^* $, as implied by the theory
c_star(y) = (1 - mlog.α * mlog.β) * y
# some useful constants
ab = mlog.α * mlog.β
c1 = log(1 - ab) / (1 - mlog.β)
c2 = (mlog.μ + mlog.α * log(ab)) / (1 - mlog.α)
c3 = 1 / (1 - mlog.β)
c4 = 1 / (1 - ab)
v_star(y) = c1 + c2 * (c3 - c4) + c4 * log(y)
function verify_true_policy(m, shocks, c_star)
k_grid = m.grid
c_star_new = coleman_egm(c_star, k_grid, m.β, m.u′, m.u′, m.f, m.f′, shocks)
plt = plot()
plot!(plt, k_grid, c_star.(k_grid), lw = 2, label = "optimal policy c*")
plot!(plt, k_grid, c_star_new.(k_grid), lw = 2, label = "Kc*")
plot!(plt, legend = :topleft)
end
verify_true_policy(mlog, shocks, c_star)
Notice that we’re passing u′ to coleman_egm twice
The reason is that, in the case of log utility, $ u'(c) = (u')^{-1}(c) = 1/c $
Hence u′ and u′_inv are the same
We can’t really distinguish the two plots
In fact it’s easy to see that the difference is essentially zero:
c_star_new = coleman_egm(c_star, mlog.grid, mlog.β, mlog.u′,
mlog.u′, mlog.f, mlog.f′, shocks)
maximum(abs(c_star_new(g) - c_star(g)) for g in mlog.grid)
Next let’s try iterating from an arbitrary initial condition and see if we converge towards $ c^* $
Let’s start from the consumption policy that eats the whole pie: $ c(y) = y $
n = 15
function check_convergence(m, shocks, c_star, g_init, n_iter)
k_grid = m.grid
g = g_init
plt = plot()
plot!(plt, m.grid, g.(m.grid),
color = RGBA(0,0,0,1), lw = 2, alpha = 0.6, label = "initial condition c(y) = y")
for i in 1:n_iter
new_g = coleman_egm(g, k_grid, m.β, m.u′, m.u′, m.f, m.f′, shocks)
g = new_g
plot!(plt, k_grid, new_g.(k_grid), alpha = 0.6, color = RGBA(0,0,(i / n_iter), 1),
lw = 2, label = "")
end
plot!(plt, k_grid, c_star.(k_grid),
color = :black, lw = 2, alpha = 0.8, label = "true policy function c*")
plot!(plt, legend = :topleft)
end
check_convergence(mlog, shocks, c_star, identity, n)
We see that the policy has converged nicely, in only a few steps
Now let’s compare the clock times per iteration for the standard Coleman operator (with exogenous grid) and the EGM version
We’ll do so using the CRRA model adopted in the exercises of the Euler equation time iteration lecture
Here’s the model and some convenient functions
mcrra = Model(α = 0.65, β = 0.95, γ = 1.5)
u′_inv(c) = c^(-1 / mcrra.γ)
Here’s the result
crra_coleman(g, m, shocks) = K(g, m.grid, m.β, m.u′, m.f, m.f′, shocks)
crra_coleman_egm(g, m, shocks) = coleman_egm(g, m.grid, m.β, m.u′,
u′_inv, m.f, m.f′, shocks)
function coleman(m = m, shocks = shocks; sim_length = 20)
g = m.grid
for i in 1:sim_length
g = crra_coleman(g, m, shocks)
end
return g
end
function egm(m, g = identity, shocks = shocks; sim_length = 20)
for i in 1:sim_length
g = crra_coleman_egm(g, m, shocks)
end
return g.(m.grid)
end
@benchmark coleman($mcrra)
@benchmark egm($mcrra)
We see that the EGM version is about 30 times faster
At the same time, the absence of numerical root finding means that it is typically more accurate at each step as well