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# st: maximum likelihood no observations error

 From andylaustata To statalist@hsphsun2.harvard.edu Subject st: maximum likelihood no observations error Date Tue, 12 Mar 2013 16:41:16 +0800 (CST)

```Dear Statalist,

Learning from Poi (2002)'s QUAIDS ml evaluator, I come up with my own ml evaluator for a new demand system that I'm currently working on.

But, while I do the ml check, it always gets stuck at Test 7 and reports no observations error (i.e. error 2000). I'm copying the code here. If anyone could give me some help, I would appreciate it very much.

By the way, if you need some data to try this ml evaluator, the food data used in Poi (2002) might be a good choice, so basically, you just type "use food, clear".

Cheers,
Ou

program define RDS1

version 11.1
args todo b lnf
//wi: shares; pi: prices; expfd: total expenditure
quietly{

tempname gammasum tausum etasum alpha_v beta_v theta_v gamma_v tau_v eta_v alpha beta theta gamma tau eta
tempvar P1 P2 P3 R1 R2 R3

local nm = \$NEQN-1

scalar `alpha_v' = `b'[1,1]
scalar `beta_v' = `b'[1,2]
scalar `theta_v' = `b'[1,3]
matrix `gamma_v' = `b'[1,(3+1)..(3+`nm')]
matrix `tau_v' = `b'[1,(3+`nm'+1)..(3+2*`nm')]
matrix `eta_v' = `b'[1,(3+2*`nm'+1)..(3+3*`nm')]

scalar `alpha' = exp(`alpha_v')
scalar `beta' = invlogit(`beta_v')
scalar `theta' = invlogit(`theta_v')
matrix `gamma' = J(1,\$NEQN,1)
matrix `tau' = J(1,\$NEQN,1)
matrix `eta' = J(1,\$NEQN,1)

scalar `gammasum' = 0 //the sum in the reparameterization of gamma
forvalues i = 1/`nm' {
scalar `gammasum' = `gammasum' + exp(`gamma_v'[1,`i'])
}

matrix `gamma'[1,1] = 1/(1+`gammasum')
forvalues i = 2/\$NEQN {
matrix `gamma'[1,`i'] = exp(`gamma_v'[1,(`i'-1)])/(1+`gammasum')
}

scalar `tausum' = 0 //the sum in the reparameterization of tau
forvalues i = 1/`nm' {
scalar `tausum' = `tausum' + exp(`tau_v'[1,`i'])
}

matrix `tau'[1,1] = 1/(1+`tausum')
forvalues i = 2/\$NEQN {
matrix `tau'[1,`i'] = exp(`tau_v'[1,(`i'-1)])/(1+`tausum')
}

scalar `etasum' = 0 //the sum in the reparameterization of eta
forvalues i = 1/`nm' {
scalar `etasum' = `etasum' + exp(`eta_v'[1,`i'])
}

matrix `eta'[1,1] = 1/(1+`etasum')
forvalues i = 2/\$NEQN {
matrix `eta'[1,`i'] = exp(`eta_v'[1,(`i'-1)])/(1+`etasum')
}

/* Form the price index variable P1.*/
gen double `P1' = p1^`gamma'[1,1]
forvalues i = 2/\$NEQN{
replace `P1' = `P1'*(p`i'^`gamma'[1,`i'])
}

/* Form the price index variable P2.*/
gen double `P2' = p1^`tau'[1,1]
forvalues i = 2/\$NEQN{
replace `P2' = `P2'*(p`i'^`tau'[1,`i'])
}

/* Form the price index variable P3.*/
gen double `P3' = p1*`eta'[1,1]
forvalues i = 2/\$NEQN{
replace `P3' = `P3'+(p`i'*`eta'[1,`i'])
}

/*gen R1*/
gen double `R1' = `alpha'*expfd^`alpha'*`P1'^(-`alpha'-1)

/*gen R2*/
gen double `R2' = `beta'*expfd^(-`beta')*`P2'^(`beta'-1)

/*gen R3*/
gen double `R3' = `theta'*expfd^(-`theta')*`P3'^(`theta'-1)

/*Now generate the error terms.*/
forvalues i = 1/`nm' {
tempvar lnl_eps`i'
gen double `lnl_eps`i'' = w`i' - (`gamma'[1,`i']*`R1'*`P1'+`tau'[1,`i']*`R2'*`P2'+`eta'[1,`i']*`R3'*p`i')/(`R1'*`P1'+`R2'*`P2'+`R3'*`P3')
}

local allofem ""
forvalues i = 1/`nm' {
local allofem "`allofem' `lnl_eps`i''"
}

/*Form sigma.*/
matrix accum sigma = `allofem', noconstant
local nobs = r(N)
matrix sigma = sigma/`nobs'

/* Finally, compute the likelihood function. */
scalar `lnf' = -1*`nobs'/2*(`nm'*(1+ln(2*_pi)) + ln(det(sigma)))
}

end

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```