Thank you for the quick response! Yes, I would like to persue this further if
possible. If you want to switch to another channel, just let me know. I include
the code below for the nbinlf.ado likelihood function (I think it's right), and
the trnbin0.ado code. I renamed the file with an 'end' suffix for endogeneity.
Likelihood function:
* 0_truncated negative binomial log likelihood function
* 17Feb1998 Joseph Hilbe STB-47 sg102
program define nbin0lfend
tempvar mu a
qui {
local lnf "`1'"
local I "`2'"
local alpha "`3'"
if "$S_mloff" != "" {
tempvar Io
qui gen double `Io' = `I' + $S_mloff
}
else local Io "`I'"
gen double `a' = exp(`alpha')
gen double `mu' = exp(`Io') * `a'
replace `lnf' = ln($S_mldepn) + ($S_mldepn - 1) * ln(`mu'/(1+`mu')) - /*
*/ ln(1+`mu')/`a' + lngamma($S_mldepn + 1/`a') - /*
*/ lngamma($S_mldepn + 1) - lngamma(1/`a') - /*
*/ ln(1-(1+`mu')^(-1/`a'))
}
end
Estimation routine:
* 0-TRUNCATED NEGATIVE BINOMIAL REGRESSION : Joseph Hilbe 17Feb1998 STB-47 sg102
* OPTIONS: tolerance,log,iteration,offset, robust, cluster, score
program define trnbin0end
version 5.0
local options "Level(integer $S_level) IRr"
if "`*'"=="" | substr("`1'",1,1)=="," {
if "$S_E_cmd"=="trnbin0end" {
error 301
}
parse "`*'"
if `level'<10 | `level'> 99 {
di in red "level() must be between 10 and 99"
exit 198
}
}
else {
local varlist "req ex"
#delimit ;
local options "`options' LTolerance(real -1) noLOg ITerate
OFfset(string) SCore(string) SCORE2 Robust CLuster(string) *";
#delimit cr
local in "opt"
local if "opt"
local weight "fweight aweight"
parse "`*'"
parse "`varlist'",parse(" ")
if "`log'"!="" { local log "quietly" }
global S_mloff "`offset'"
tempvar touse mysamp
tempname b f V
mark `touse' `if' `in'
markout `touse' `varlist' `offset'
if "`weight'" != "" {
tempvar wvar
gen double `wvar' `exp'
}
else local wvar 1
if ("`weight'"=="aweight") {
qui sum `wvar'
qui replace `wvar' = `wvar'/_result(3)
}
if "`offset'" != "" {
local ovar "`offset'"
}
else local ovar 0
global S_cen "`censor'"
* ESTIMATION OF LL0
quietly {
qui poisson `1'
mat coef=get(_b)
eq `1': `1'
eq lnalpha:
ml begin
ml function nbin0lfend
ml method lf
ml model `b'= `1' lnalpha, from(coefs) depv(10)
ml sample `mysamp' [`weight' `exp'] if `touse'
`log' ml maximize `f' `V', `options'
local lf0 = `f'
drop `mysamp'
}
* ESTIMATION OF FULL MODEL
qui poisson `varlist'
local pll = $S_E_ll
mat coef=get(_b)
eq `1': `varlist'
eq lnalpha:
ml begin
ml function nbin0lfend
ml method lf
ml model `b' = `1' lnalpha, from(coefs) depv(10)
ml sample `mysamp' [`weight' `exp'] if `touse'
`log' ml maximize `f' `V', `options'
#delimit ;
ml post nbin0, title("0-Truncated Negative Binomial w/ End. Strat.
Estimates")
lf0(`lf0') pr2;
#delimit cr
local nbll = $S_E_ll
global S_1 = 2*(`nbll' - `pll')
* ROBUST CALCULATIONS
local y "`1'"
mac shift
global rhs `*'
tempname b V
tempvar xb e1 e2 muvar alpha amuvar digam1 digam2
mat `b' = get(_b)
mat `V' = get(VCE)
predict double `xb', index
* SCORE VARIABLES
qui gen `muvar' = exp(`xb' + `ovar')
qui gen `alpha' = exp([lnalpha]_b[_con])
qui gen `amuvar' = `muvar'*`alpha'
#delimit ;
qui gen `digam1' = ( lngamma( (`y'+1/`alpha') +.0001)-
lngamma((`y'+1/`alpha')-.0001) )/.0002;
qui gen `digam2' = ( lngamma(1/`alpha' +.0001)-
lngamma(1/`alpha'-.0001) )/.0002;
#delimit cr
*SCORES
tempvar tr0 tra ialpha
qui gen double `ialpha' = -1/`alpha' if `touse'
#delimit ;
qui gen double `tr0' = ( `muvar' * -(1+`amuvar')^(-(1+`alpha')/
`alpha') ) / (-1+(1+`amuvar')^(`ialpha')) if `touse';
qui gen double `tra' = - (1 +`amuvar')^ (`ialpha') *
( 1/(`alpha'^2) * ln(1+`amuvar') - (`muvar'/ (`alpha'*
(1+`amuvar')))) / ( 1-(1+`amuvar')^(`ialpha')) if `touse';
qui gen double `e1' = (`y' - `muvar')/(1+ `amuvar') - `tr0'
if `touse';
qui gen double `e2' = `ialpha'* ( (`alpha' * (`muvar'-`y'))/
(1+`amuvar')) - ln(1+`amuvar') + `digam1' - `digam2' -
`tra' if `touse';
#delimit cr
* SCORE FUNCTIONS TO FILE
if "`score'"!="" {
gen `score' = `e1'
}
* CLUSTER OPTIONS
if "`robust'"!="" & "`cluster'"=="" {
_robust `e1' `e2' if `touse'
qui test $rhs, min
local ch2 = _result(6)
}
if "`cluster'"!="" {
_robust `e1' `e2' if `touse', cluster(`cluster')
qui test $rhs, min
local ch2 = _result(6)
}
}
* OUTPUT
if "`irr'"!="" {
local eform "eform(IRR)"
}
ml mlout nbin0, level(`level') `eform'
di in gr " alpha " in ye %9.0g exp([lnalpha]_b[_cons]) /*
*/ in gr " [lnalpha]_cons = ln(alpha)"
di _col(21) in gr "(LR test against Poisson, chi2(1) = " in ye %9.0g /*
*/ $S_1 /*
*/ in gr " P = ", in ye %6.4f chiprob(1,$S_1) in gr ")"
end
[email protected]
Sent by: To: [email protected]
owner-statalist@hsphsun2. cc:
harvard.edu Subject: Re: st: Truncated negative binomial with endogenous stratification
04/28/2005 12:57 PM
Please respond to
statalist
I am interested in your problem. I suspect that the log-likelihood formula
used in nbinlf.ado for estimating the zero truncated negative binomial is OK.
It's not a difficult formula and it was checked many times prior to
distribution. I am wondering if the problem you are experiencing relates to the
older
code in which the program was written. I believe it was version 5 or 6. I
had to entirely re-write my censored Poisson regression algorithm using version
8 code in order to add certain survey capabilities to it, including
pweights. I renamed it cepois, to distinguish it from the 1997 version,
cenpois, and
later posted it to the Boston SSC site. I'll be happy to help with your
project of modifying the code to allow endogenous stratification. I suspect,
though, that it will entail a re-write to version 8 code. Or we can see what
capabilities version 9 has to make the project easier.
If you are interesting, contact me privately and we'll go from there.
Joe Hilbe
====================================
I attempted to modify Joseph Hilbe's 0-truncated negative binomial routine
(nbinlf.ado likelihood function) to accomodate for endogenous stratification
(e.g. on-site sampling), however I am running into problems during the
maximization process; probably inherent in the trnbin0.ado file which
performs
the estimation. Has anyone else attempted to do this? Would anyone be
able to
help?
Cheers,
Craig Meisner
-------------------------------------------------------------
Craig Meisner
The World Bank
Development Economics Research Group
Infrastructure and Environment
MSN MC2-205, 1818 H Street, NW
Washington DC 20433
Tel: 202-473-6852, Fax: 202-522-3230
E-mail: [email protected]
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