
This post has NOT been accepted by the mailing list yet.
Dear Statalisters,
I've been trying to estimate a Double Hurdle model on alcohol expenditure data.
craggit by William Burke allows for hetereschedasticity but not for nonnormality. So, I guess estimates would still be subject to bias.
I then came across dh written by Moffatt (2005) in the Journal of the Operational Research Society, Vol.56, No.9 (*see below the command). dh allows for nonnormality by applying a BoxCox transformation to the dependant variable.
I'd like to ask you the following question:
I was told that, to deal with nonnormality, the Inverse Hyperbolic Sine transformation is a better solution than the BoxCox transformation. Is that true? If so, why?
Also, Moffatt's Stata code implies homoscedasticity (sigma is an invariant parameter). Will the estimates be consistent still?
I'd greatly appreciate your help.
Stefano Verde
*
program define dh
version 6
args Inf thetal theta2 theta3 theta4
tempvar d p z p0 pll yt
quietly gen double 'd'= $ML_yl >0
quietly gen double 'p'= normprob('theta3')
quietly gen double '1'= 'theta4'
quietly gen double 'yt'= ($MLyl^'I'1)/'l'
quietly gen double 'z'= ('yt''thetal' )/('theta2')
quietly gen double 'pO'= 1('p'*normprob('z'))
quietly gen double 'pl' = (($ML_yl + (1'd'))^
('T'1))*'p'*normd('z')/'theta2'
quietly replace 'lnf = ln((1'd')*'p0+' 'd'*'pl')
end
ml model If dh (y = 'listy') () (d  'listd') ()
ml init b, copy
ml maximize
Notes: 'listy' is a previously defined list of variables
appearing in the second hurdle; 'listd' contains the variables
of the first hurdle. 'thetal' corresponds to xi'f# in (14),
'theta2' to a, 'theta3' to zi'a, and 'theta4' to .. b is a vector of
suitable starting values.
