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Re: Re: st: Re:Two hurdle models

From   [email protected]
To   [email protected]
Subject   Re: Re: st: Re:Two hurdle models
Date   Mon, 7 Jan 2008 16:10:45 -0500

Thank you very much, Nicola. I will try it and see what happens.


Development Research, World Bank
Email: [email protected] or branko_mi@yahoo.
tel: 202-473-6968
World Bank, Room MC 3-559
1818 H Street NW
Washington D.C. 20433

For "Worlds Apart" see


For papers see also:

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             owner-statalist@hsp                                             To 
              [email protected]         
                                         [email protected]               
             01/07/2008 12:14 PM                                        Subject 
                                         Re: Re: st: Re:Two hurdle models       
              Please respond to                                                 

I like collecting software and I downloaded -dhurdle-, too. I never used it, but
I wrote an help (just in case I am going to use it!) which I report below. I
mixed information from the webpages from which I downloaded it and information
from other commands for most used options (e.g. robust).
Obviously, since I never used the command, and I don't even have much of the
statistical knowledge underlying it, you can use it as a guidance - at YOUR OWN
RISK!!! I don't know what option -bracket- is and how it works.
You'd be better off contacting Fennema direclty for an extra help/a review of my
work. The command is quite old:
. which dhurdle
*! version 1.0 12 Feb 2003


Double-hurdle model (model with selection and censoring)

dhurdle varlist [weight]  [if exp] [in range] [, select(d = varlist_s)
cluster(varname) nocoef noconstant first from(init_values) level(integer)
nolog mlmethod(method) mlopts(option) offset(varname) robust score(string)
technique(algorithm_spec) ]

    pweights, aweights, fweights, and iweights are allowed; see help weights.


    Censoring of the dependent variable is traditionally dealt with using the
    model. Cragg (1971) proposed the extension that the probability of a zero
    realisation, is not directly to the density for a continuous realisation,
    instead govern me other process. The original model made the assumption that
    two error terms were jointly normal and uncorrelated. Similar to that
    by McDowell (2003) for count models, the separability of the likelihood
    permits the use bination of commands to estimate this model: truncreg and
    This assumption has been relaxed in later work, e.g. Jones (1992).  Both
parts of
    this likelihood function must, however, be maximised simultaneously; there
is no
    two-step equivalent. This is available using the dhurdle command.  Note, of
    course, the difficulties of these procedures in achieving convergence. See
    and Gr�sj�, 2001, "A Monte Carlo Simulation of Tobit models", Applied
    Letters, 8, pp.581-584 for an assessment of the problems of


    select(d = varlist_s) specifies selection equation: dependent and
        variables. d is a dummy taking the value of 1 if the dependent variable
        greater than zero, 0 otherwise. This option is required.

    independent estimates a double hurdle model with independent errors, the
        Cragg formulation.

    bracket �

    cluster(varname) specifies that the observations are independent across
        (clusters) but not necessarily within groups.  varname specifies to
        group each observation belongs.  See [U] 23.14 Obtaining robust variance

    nocoef does not display the coefficient table; seldom used.

    noconstant supresses constant term.

    first reports first-step estimates.

    from(init_values) specifies initial values for the coefficients.  You can
        the initial values in one of three ways: by specifying the name of a
        containing the initial values (e.g., from(b0) where b0 is a properly
        vector); by specifying coefficient names with the values (e.g.,
        /sigma=7.4)); or by specifying a list of values (e.g., from(2.1 7.4,
        from() is intended for use when you are doing bootstraps (see bootstrap)
        in other special situations (e.g., with iterate(0)).  Even when the
        specified in from() are close to the values that maximize the
likelihood, only
        a few iterations may be saved.  Poor values in from() may lead to

    level(integer) sets confidence level; default is level(95).

    noheader suppresses display of the header reporting the estimation method
and the
        table of equation summary statistics.

    nolog prevents the iteration log from being shown.

    mlmethod(method) where method is { lf | d0 | d1 | d1debug | d2 | d2debug }.
        help mlmethod.

    mlopts(option) specifies ml options.

        nowarning suppresses "convergence not achieved" message of iterate(0).

        novce substitutes the zero matrix for the variance matrix.

        score(newvars) new variables containing the contribution to the score.

    offset(varname) includes varname in model with coefficient constrained to 1.

    robust specifies that the Huber/White/sandwich estimator of variance is to
be used
        in place of the traditional calculation; see [U] 23.14 Obtaining robust
        variance estimates.

    score(newvars) calculates the equation-level score; the derivative of the
        likelihood with respect to the linear prediction.

    noskip specifies that a full maximum-likelihood model with only a constant
for the
        regression equation be fitted. This model is not displayed but is used
as the
        base model to compute a likelihood-ratio test for the model test
        displayed in the estimation header. By default, the overall model test
        statistic is an asymptotically equivalent Wald test of all the
parameters in
        the regression equation being zero (except the constant).  For many
        this option can substantially increase estimation time.

    technique(algorithm_spec) specifies how the likelihood function is to be
        maximized. The following algorithms are currently available.

        technique(nr) specifies Stata's modified Newton-Raphson (NR) algorithm.

        technique(bhhh) specifies the Berndt-Hall-Hall-Hausman (BHHH) algorithm.

        technique(dfp) specifies Davidon-Fletcher-Powell (DFP) algorithm.

        technique(bfgs) specifies the Broyden-Fletcher-Goldfarb-Shanno (BFGS)

        The default is technique(nr).

        You can switch between algorithms by specifying more than one in the
        technique() option.  By default, dhurdle will use an algorithm for five
        iterations before switching to the next algorithm.  To specify a
        number of iter nclude the number after the technique in the option.  For
        example, specifying technique(bhhh 10 nr 1000) requests that dhurdle
        10 iterations using the BHHH algorithm perform 1000 iterations using the
        algorithm, and then swi to BHHH for 10 iterations, and so on.  The
        continues until convergence or until the maximum number of iterations is

At 02.33 05/01/2008 -0500, you wrote:
>Thanks a lot, Wilner. This is most helpful.  I thought that the two-hurdles
>models have to be estimated jointly, but with independent estimation, Stata
>commands should suffice. Thanks again!
>Best regards,

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