Hi all,
I'm using STATA to do MLE and I'm using the lf method.
I have only 1 variable, interest rate and it is explained only as a function of its previous value. The coefficients however are complex and non-linear thus I need to hard code the lag into the likelihood function. I'm not sure whether I've done this correctly since I get the following result when I run my do file.
initial: log likelihood = -<inf> (could not be evaluated)
could not find feasible values
r(491);
The process that the interest rate follows is this
tbrate = exp(beta)*L.tbrate + (alpha/beta)*(exp(beta)-1) + e
where e is an error term. The likelihood function is in the paper and is based on the form of tbrate and the properties of the error term.
Here is a copy of my code. If anyone has more experience with this and might be able to help I'd appreciate it.
tbrate is the interest rate variable and ltbrate is its lagged values.
capture program drop myest
program define myest
version 6
args lnf theta1 theta2 theta3 theta4
tempvar a b c d e
quietly gen double `a'= `theta3'/(2*`theta2')
quietly gen double `b'= exp(2*`theta2') - 1
quietly gen double `c'= 2*`theta4'
quietly gen double `d'= exp(`theta2')
quietly gen double `e'= `theta1'/`theta2'
quietly replace `lnf' = (ln(`a'*`b'*(ltbrate)^`c')) + (($ml_y1-`d'*ltbrate-`e'*(`d'-1))^2)/(`a'*`b'*(ltbrate)^`c')
end
ml model lf myest (tbrate) /alpha /beta /sigma2 /gamma
ml maximize
Regards,
Peter Aling
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