[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
[email protected] |

To |
[email protected] |

Subject |
Re: Re: st: Re:Two hurdle models |

Date |
Mon, 07 Jan 2008 17:52:00 +0100 |

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 c:\ado\plus\d\dhurdle.ado *! version 1.0 12 Feb 2003 Nicola Double-hurdle model (model with selection and censoring) -------------------------------------------------------- dhurdle varlist [weight] [if exp] [in range] [, select(d = varlist_s) independent cluster(varname) nocoef noconstant first from(init_values) level(integer) noheader nolog mlmethod(method) mlopts(option) offset(varname) robust score(string) noskip technique(algorithm_spec) ] pweights, aweights, fweights, and iweights are allowed; see help weights. Description Censoring of the dependent variable is traditionally dealt with using the Tobit model. Cragg (1971) proposed the extension that the probability of a zero realisation, is not directly to the density for a continuous realisation, but instead govern me other process. The original model made the assumption that the two error terms were jointly normal and uncorrelated. Similar to that demonstrated by McDowell (2003) for count models, the separability of the likelihood function permits the use bination of commands to estimate this model: truncreg and probit. 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 Flood and Gr�sj�, 2001, "A Monte Carlo Simulation of Tobit models", Applied Economics Letters, 8, pp.581-584 for an assessment of the problems of misspecification. Options select(d = varlist_s) specifies selection equation: dependent and independent variables. d is a dummy taking the value of 1 if the dependent variable is greater than zero, 0 otherwise. This option is required. independent estimates a double hurdle model with independent errors, the original Cragg formulation. bracket � cluster(varname) specifies that the observations are independent across groups (clusters) but not necessarily within groups. varname specifies to which group each observation belongs. See [U] 23.14 Obtaining robust variance estimates. 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 specify the initial values in one of three ways: by specifying the name of a vector containing the initial values (e.g., from(b0) where b0 is a properly labeled vector); by specifying coefficient names with the values (e.g., from(age=2.1 /sigma=7.4)); or by specifying a list of values (e.g., from(2.1 7.4, copy)). from() is intended for use when you are doing bootstraps (see bootstrap) and in other special situations (e.g., with iterate(0)). Even when the values 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 convergence problems. 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 }. See 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 log 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 statistic 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 models, 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) algorithm. 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 different number of iter nclude the number after the technique in the option. For example, specifying technique(bhhh 10 nr 1000) requests that dhurdle perform 10 iterations using the BHHH algorithm perform 1000 iterations using the NR algorithm, and then swi to BHHH for 10 iterations, and so on. The process continues until convergence or until the maximum number of iterations is reached. 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, >Branko * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:

- Prev by Date:
**st: weird error in -nlcom-** - Next by Date:
**st: -nlcom- (more)** - Previous by thread:
**Re: st: Re:Two hurdle models** - Next by thread:
**Re: Re: st: Re:Two hurdle models** - Index(es):

© Copyright 1996–2024 StataCorp LLC | Terms of use | Privacy | Contact us | What's new | Site index |