[Gretl-users] Monte Carlo Simulation
Dear all,
I have been having some troubles computing a Monte Carlo simulation.
I am a student, so please don't be too horrified by my short experience :)
The problem is the following:
I am requested to perform a MC simulation on a lagged-dependent variable
model with autocorrelated residuals. The estimator for the lagged-dependent
variable, the only regressor, should be inconsistent and biased.
My concern is that decreasing the sample size the bias decreases, and I
guess that that's just wrong.
(I also tried using robust std errors, and nothing changes!)
here's the code I used:
nulldata 200
setobs --time-series
scalar n=$nobs
scalar beta1=0.8
scalar rho=0.4
smpl 1 1
series u=0
series y=0
smpl 2 200
series e=randgen(N,0,1)
series u=rho*u(-1)+e
series y=beta1*y(-1)+u
ols y y(-1) --simple
loop 5000 --progressive --quiet
series e=randgen(N,0,1)
series u=rho*u(-1)+e
series y=beta1*y(-1)+u
ols y y(-1)
scalar beta1hat= $coeff(y_1)
scalar s1= $stderr(y_1)
scalar bias=$coeff(y_1)-beta1
genr LB = beta1hat - critical(t,$df,0.025)*s1
genr UB = beta1hat + critical(t,$df,0.025)*s1
print beta1 beta1hat bias LB UB
endloop
smpl 50 --random --replace
loop 5000 --progressive --quiet
series e=randgen(N,0,1)
series u=rho*u(-1)+e
series y=beta1*y(-1)+u
ols y y(-1)
scalar beta1hat= $coeff(y_1)
scalar s1= $stderr(y_1)
scalar bias=$coeff(y_1)-beta1
genr LB = beta1hat - critical(t,$df,0.025)*s1
genr UB = beta1hat + critical(t,$df,0.025)*s1
print beta1 beta1hat bias LB UB
endloop
Thank you so much
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