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