Hi, statalisters.
I am trying to build a Maximum likelihood model. It is a binary choice model where a choice depends on utilities of alternative. First alternative (sv) is a utility of "offer" and a second one (cv) is a simple average of "offer_hat`j'". There are 680, 364, 165, 56, 10 and 2 variables "offer_hat`j'" in 1st, 2nd and so on periods respectively. Prior to estimation I have generated variable "i" that contains 680, 364, 165, 56, 10 and 2 for the respective time periods.
Now I am sure that the utility function is strictly concave, it is continuous, however when estimating it gives me error "could not calculate numerical derivatives, missing values encountered".
Please find below the script I am using. May be you can tell me what I am doing wrong? May be one more thing to mention is that dataset I have is unbalanced panel.
Thank you very much indeed.
Alex
capture program drop utility
program define utility
args todo b lnf
tempvar alpha beta W cv sv lnfj temp sigma
tempname i
mleval `alpha'=`b', eq(1) scalar
mleval `beta'=`b', eq(2) scalar
mleval `W'=`b', eq(3) scalar
qui gen double `sv' = (1-exp(-`alpha'*(`W'+offer)^(1-`beta')))/`alpha'
*qui gen `i'=comb(22-stage_id,3) if stage_id!=20
*qui replace `i'= 2 if stage_id==20
qui gen double `cv' = 0
qui gen double `temp' = 0
qui gen double `sigma' = 0
forval j=1/680 {
qui replace `temp' = (1-exp(-`alpha'*(`W'+offer_hat`j')^(1-`beta')))/`alpha' if time==1
qui replace `cv' =`cv' + `temp' if time==1
}
forval j=1/364 {
qui replace `temp' = (1-exp(-`alpha'*(`W'+offer_hat`j')^(1-`beta')))/`alpha' if time==2
qui replace `cv' =`cv' + `temp' if time==2
}
forval j=1/165 {
qui replace `temp' = (1-exp(-`alpha'*(`W'+offer_hat`j')^(1-`beta')))/`alpha' if time==3
qui replace `cv' =`cv' + `temp' if time==3
}
forval j=1/56 {
qui replace `temp' = (1-exp(-`alpha'*(`W'+offer_hat`j')^(1-`beta')))/`alpha' if time==4
qui replace `cv' =`cv' + `temp' if time==4
}
forval j=1/10 {
qui replace `temp' = (1-exp(-`alpha'*(`W'+offer_hat`j')^(1-`beta')))/`alpha' if time==5
qui replace `cv' =`cv' + `temp' if time==5
}
forval j=1/2 {
qui replace `temp' = (1-exp(-`alpha'*(`W'+offer_hat`j')^(1-`beta')))/`alpha' if time==6
qui replace `cv' =`cv' + `temp' if time==6
}
qui replace `cv'=`cv'/i
qui egen double `sigma'= sd(`cv' - `sv')
qui gen double `lnfj' = ln(normprob((`cv' - `sv')/sigma)) if $ML_y1==0
qui replace `lnfj' = ln(normprob((`sv' - `cv')/sigma)) if $ML_y1==1
mlsum `lnf' = `lnfj'
end
ml model d0 utility (deal=) /beta /W
ml maximize
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