After I had some problems in the beginning, I sort of gave up. Now I want to give it another try and it would be really nice to get to the solution of the problem this time.

I have a basic root-finding problem. It is an economics problem and broadly speaking I want to know what “optimal” consumption is depending on two state variables, capital and productivity.

Here is the code:

```
# This example: Neoclassical Growth Model
using QuantEcon
# Parameters
global γ = 2; # Risk aversion
global α = 0.3; # Capital share
global β = 0.98; # Discount factor
# First, create a grid using the QuantEcon toolbox
K_grid = collect(linspace(0.1, 10, 5))
z_grid = collect(linspace(0.9, 1.1, 3))
p_grid = [1/3 1/3 1/3] # iid shocks and they are equally probable
grid=gridmake(K_grid,z_grid)
# First, Rootfinding and linear policy function for c.
using NLsolve
f! = function(α_N,real_res,grid) # we want to find roots for f
global γ,α,β
K_next = grid[:,2].*grid[:,1].^α - (grid*α_N)
next_grid = gridmake(K_next,z_grid)
c_next = (next_grid*α_N)
RHS = β*reshape(c_next.^(-γ).*α.*(next_grid[:,1]).^(α-1).*next_grid[:,2],15,3)*p_grid'
res = (grid*α_N).^(-γ) - RHS
real_res = grid'*res
end
g!(α_N,real_res) = f!(α_N,real_res,grid)
guess = [0.01, 0.1]
res = nlsolve(g!,guess)
g!(guess,[0,0])
```

The output from the second to last line is:

nlsolve claims that the residual f(x) is already very small and convergence has been achieved. However, if I execute `g!(guess,[0,0])`

, I get the following:

So the residual is by no means close to zero.

Does anyone know how I can go about it?