OK, I took into consideration what people said in this group.
I changed the code (see below) and I get results.
I want to save the results in a .csv file, but instead of getting three
columns, I get all the results in one column.
sigma = 1.5; # utility parameter
delta = 0.1; # depreciation rate
beta = 0.95; # discount factor
alpha = 0.30; # capital elasticity of outp
nbk = 1000; # number of data points in t
crit = 1; # convergence criterion
epsi = 1e-6; # convergence parameter
ks = ((1-beta*(1-delta))/(alpha*beta))^(1/(alpha-1));
dev = 0.9; # maximal deviation from ste
kmin = (1-dev)*ks; # lower bound on the grid
kmax = (1+dev)*ks; # upper bound on the grid
dk = (kmax-kmin)/(nbk-1); # implied increment
kgrid = linspace(kmin,kmax,nbk)'; # builds the grid
v = zeros(nbk,1); # value function
dr = zeros(nbk,1); # decision rule (will contain indices)
tv = zeros(nbk,1)
while crit > epsi;
for i = 1:nbk
#compute indexes for which consumption is positive
tmp = (kgrid[i]^alpha+(1-delta)*kgrid[i]-kmin);
imax = min(floor(tmp/dk)+1,nbk);
#consumption and utility
c = kgrid[i]^alpha+(1-delta)*kgrid[i]-kgrid[1:imax];
util = (c.^(1-sigma)-1)/(1-sigma);
# find value function
(tv[i]) = maximum(util+beta*v[1:imax]);
end;
crit = maximum(abs(tv-v)); # Compute convergence criterion
v = tv; # Update the value function
end
# Final solution
# I want to save the results
csvfile = open("neoclassical.csv","w")
write(csvfile,"capital,consumption,utility, \n")
kp = kgrid;
c = kgrid.^alpha+(1-delta)*kgrid-kp;
util= (c.^(1-sigma)-1)/(1-sigma);
results = kp', c', util'
println(csvfile, join(results,","), "\n")
close(csvfile)
