Dear Statalisters,
I am running a maximization procedure to maximize benefit of Y with Xb
<= percentiles 16. I first run the regular ml model (least squares
with normal error) to get the betas from first model. I then use these
betas as initial values for the second ml. Using my dataset, consist
of 8635 observations, the log-likelihood after a number of iterations
takes a value which does not change over subsequent iterations I have
to break the procedure.
I don't know what is the problem, It's my first time using ml
procedure. Could someone have a way to solve this problem ?
Best regard,
Hendro
Here is the program:
version 9.2
set more off
clear
set mem 256m
cap log using treatment-b-exp2,replace text
cap program drop maxreg1
program maxreg1
args todo b lnf
tempvar xb lnfj temprank d
tempname mu lnmu
mleval `xb' = `b', eq(1)
mleval `lnmu' = `b', eq(2) scalar
scalar `mu' = exp(`lnmu')
qui gen double `lnfj' = ln(normd($ML_y1,`xb',`mu'))
mlsum `lnf' = `lnfj'
end
cap program drop maxreg
program maxreg
args todo b lnf
tempvar xb lnfj temprank d
mleval `xb' = `b', eq(1)
gsort +`xb' +$ML_y1
g `temprank' = _n
qui count if `xb' != .
local tempcount = r(N)
qui g `d' = (`temprank' < `tempcount' * 0.16)
qui gen `lnfj' = $ML_y1 * `d'
mlsum `lnf' = `lnfj'
end
cap program drop mutility
program define mutility
syntax anything(name=pcexp), [pl(varname) rho(real 12345)
alpha(real 12345)] [gen(varname)]
if "`gen'"!="" {
local genvar = "`gen'"
}
else {
local genvar = "mutility"
}
gen double `genvar'=cond(pcexp <= `pl',pcexp^-`rho',`alpha'*(pcexp^-`rho'))
end
use if b1r1==16 using k:\targetting\sus2006\indics06,clear
mutility pcexp,pl(povline) rho(2) alpha(0.5)
ml model d0 maxreg1 (xb : mutility = house troof1 troof2 twall tfloor
pcfloor water toilet septic lighting) (lnmu:)
ml max
matrix startval = e(b)
ml model d0 maxreg (xb : mutility = house troof1 troof2 twall tfloor
pcfloor water toilet septic lighting)
ml init startval, skip
ml max, difficult
log close
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