[Limdep Nlogit List] double hurdle again

[Vassilopoulos Achilleas]

Brett,

The estimation of double-hurdle models is straightforward since all you need
is a discrete choice model using all observations and estimation of the
parameters of the density f (y|m = 1, x) using only observations with y > 0.
Below is a code to run a double-hurdle model and perform a chi-squared test
to see whether the double hurdle model is more appropriate than Tobit.

Namelist ; x = .......... $
Tobit 	; Lhs = y ; Rhs = x $
Calc	; LTobit  =  logl $
Create; m = y > 0 $
Probit ; Lhs=m; Rhs=x $
Calc ; List ; LProbit=Logl $
Truncate ; Lhs=y ; Rhs=x $
Calc ; List ; LTrunc=Logl
; LRtest=2*((LProbit+LTrunc)-LTobit) $
; prob = Chi(LRtest,df) $


best,
_____________ - _______________

Achilleas Vassilopoulos

-----Original Message-----
From: limdep-bounces at limdep.itls.usyd.edu.au
[mailto:limdep-bounces at limdep.itls.usyd.edu.au] On Behalf Of Brett Smith
Sent: 08 June, 2010 06:05
To: 'Limdep and Nlogit Mailing List'
Subject: [Limdep Nlogit List] double hurdle again

Dear List, 

I asked a question about the double hurdle model a week or so ago. The
reason being that it is unlikely (fails likelihood test) that the
restrictions of the Tobit regression apply to the data. 
After fishing though the manuals I cannot see how to run a double hurdle
model in limdep. I am sure it is obvious...but just not seeing it. I have
added some code below which is using the minimize function (based on Jones
1992). 

I would like to ask the group if this is the correct (only, easiest) way to
estimate the double hurdle.

Thank you again,

Brett 



CREATE;ZERO = 0$

NAMELIST	;ProbReg = Z, ZERO, ZERO....? Repeat zero for the number of
vars in X
		;TrunReg = ZERO....,X,ZERO$  ? repeat zero for the number of
vars in Z, and one for SE in truncated regression

?run probit and  censored regresseion for start values

Probit; LHS= Travel; Rhs=Z$
Matrix; Bprob=b$? save probit parameters
Trunc; Lhs=y; Rhs=X$
Matrix; btrunc=b$? 
Matrix; bstart= bprob,_btrunc $ ? can't remember syntax but combine the two
vectors 

?Double hurdle model
MINIMIZE	;START= bstart
		;LABELS = B1 ,B2, B3, .... ,SE
		;FCN = - (1 - Travel)*LOG( 1 - PHI(b1'ProbReg)*PHI(
(b1'TruncReg)/SE) ))          ?log likelihood for travel
			 - Travel*(LOG(PHI(b1'ProbReg)) + LOG(NOl((Y -
(b1'TruncReg)/SE)))/SE)$    ? Log likelihood for observations greater than
zero




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