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st: ivpois- received a coefficientfor for var. that wasn't specified


From   "Anat (Manes) Tchetchik" <anatmanes@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: ivpois- received a coefficientfor for var. that wasn't specified
Date   Wed, 3 Oct 2012 15:13:10 +0200

Austin Hi,

I have ran the ivpois and received a coefficient for a var, which
wasn't specified: years_abroad. What am I missing here?


 ivpois  met_abroad lnage US italy france brussiarussia
other_west_EUUK  income obligation FUN  ln_ years_ abroad ,
exog(FR_return  FR_native FR_work FR_student FR_immigrant livealone
first_deg_family secnd_deg_family third_deg_family forth_deg_family
flighttime native_israel have_kids Health male education religious
south_reside) endog(facebook email  phone skype_msn)
----------------------------------------------------------------------------------------------------------
  met_abroad |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+-------------------------------------------------------------------------------------------
met_abroad
       lnage |           -1.447381    .719936    -2.01   0.044
-2.85843   -.0363323
          US |           -.0170843   .3448535    -0.05   0.960
-.6929847    .6588161
       italy |             -.4411522   .5102456    -0.86   0.387
-1.441215    .5589108
      france |          .3109442   .4994053     0.62   0.534
-.6678721    1.289761
brussiarussia |       1.473384   .5620763     2.62   0.009
.3717342    2.575033
other_west_euuk | .2303449   .4372421     0.53   0.598    -.6266338    1.087324
      income |         .0791544   .0827231     0.96   0.339
-.0829799    .2412887
  obligation |          -.0023054   .1242432    -0.02   0.985
-.2458175    .2412068
         FUN |           .2132838   .0896393     2.38   0.017
.0375939    .3889737
ln_years_abroad |   .8137992   .1697787     4.79   0.000     .4810391
  1.146559
years_abroad |      -.0317331   .0140841    -2.25   0.024    -.0593373
  -.0041288
    facebook |         -.8209555   .2368495    -3.47   0.001
-1.285172   -.3567389
       email |           -.1442276   .1544278    -0.93   0.350
-.4469006    .1584454
       phone |          .1591537   .1312153     1.21   0.225
-.0980235     .416331
   skype_msn |      .2806095   .1348213     2.08   0.037     .0163646
  .5448543
       _cons |           6.175723   3.350298     1.84   0.065
-.3907397    12.74219
------------------------------------------------------------------------------

Any hint will be greatly apprecuated,
Anat



On Tue, Sep 25, 2012 at 3:32 PM, Austin Nichols <austinnichols@gmail.com> wrote:
>
> Anat (Manes) Tchetchik <anatmanes@gmail.com>:
> 1. You can specify that max as exposure, but it sounds a bit odd to
> me--why not just include ln(number_of_years_rel_abroad) and ln(age-17)
> as predictors and let the regression estimate the coefs?  If one coef
> turns out to be close to one, then it looks like an exposure variable:
> an exposure X just means that ln(X) enters with a coef of one
> (elasticity of one). An offset X just means that X enters with a coef
> of one (semi-elasticity of one).
> 3. Yes, predict yhat and calculate squared corr of y and yhat as a
> pseudo-R-squared.
>
> On Tue, Sep 25, 2012 at 7:49 AM, Anat (Manes) Tchetchik
> <anatmanes@gmail.com> wrote:
> > Austin hi,
> > I have read the material regarding the ivpois model you have referred
> > me to and ran it on my data set. The results look pretty plausible .
> > Just before sticking to this model I have few last questions:
> >
> > 1. Can I use the exposure variable (in my case the maximum of the two
> > values: respondent's adulthood years and and the no. of years his/her
> > relative is staying abroad) also as an independent var.? (or given
> > that I do it- I should not use the exposure option)
> >
> > 2. I'm not sure i understand when I should use the exposure option and
> > when the offset one
> >
> > 3. Do I calculate goodness of fit measure using the same procedure you
> > recommended earlier
> > (http://www.stata.com/support/faqs/statistics/r-squared/) ?
> >
> > Thank you very much!
> > Anat
> >
> > On Mon, Sep 24, 2012 at 6:38 PM, Austin Nichols <austinnichols@gmail.com> wrote:
> >>
> >> Anat (Manes) Tchetchik <anatmanes@gmail.com> :
> >> Indeed, no censoring in your model. You have exactly the case I
> >> referred to when I wrote "I suspect you have a lower limit at zero
> >> which is actually a very low conditional mean rounded down to zero."
> >> Unless you believe that the Tobit model somehow correctly captures
> >> bunching at zero due to utility functions which would imply a negative
> >> demand for travel abroad, if such a thing were possible, which is
> >> implausible at best, you are much better off assuming that people with
> >> zero travel abroad simply have very low demand, and a group that has a
> >> conditional mean of 1/100000 trips will indeed have a lot of zeros
> >> observed.  The -ivpois- package on SSC, and the -gmm- specifications
> >> that supersede it, are designed to allow instrumental variables in a
> >> count model, or a regression with nonnegative outcomes more generally:
> >> http://www.stata.com/meeting/boston10/boston10_nichols.pdf
> >> http://fmwww.bc.edu/repec/bocode/i/ivpois.html
> >>
> >> On Mon, Sep 24, 2012 at 10:54 AM, Anat (Manes) Tchetchik
> >> <anatmanes@gmail.com> wrote:
> >> > I haven't thought about the count model, I will definitely try to run
> >> > it! thanks much!
> >> >
> >> > On Mon, Sep 24, 2012 at 5:38 PM, Maarten Buis <maartenlbuis@gmail.com> wrote:
> >> >> That does not sound like censoring at all. I would think of this as a
> >> >> regular count model. There are examples on how to deal with such an
> >> >> iv-model in -help gmm-.
> >> >>
> >> >> Hope this helps,
> >> >> Maarten
> >> >>
> >> >> On Mon, Sep 24, 2012 at 4:11 PM, Anat (Manes) Tchetchik
> >> >> <anatmanes@gmail.com> wrote:
> >> >>> Austin Hi,
> >> >>> Thank you very much for your reply!
> >> >>> What I have as a dependent var. are 500 respondents' reports of the
> >> >>> number of times they travelled abroad to visit their friends and
> >> >>> relatives over the course of their adult lives.  Some respondents yet,
> >> >>> who have relatives abroad, did not travel at all.
> >> >>> So the observations are censored at zero, with mean =2.2, max =50 and
> >> >>> stdev= 3.8.
> >> >>> Do you think in that case that the general methods of moments will be better?
> >> >>> Thanks much!!!
> >> >>> Anat
> >> >>>
> >> >>> On Sun, Sep 23, 2012 at 5:49 AM, Austin Nichols <austinnichols@gmail.com> wrote:
> >> >>>>
> >> >>>> Anat (Manes) Tchetchik <anatmanes@gmail.com>:
> >> >>>> You can always -predict- and compute the squared correlation of
> >> >>>> predictions with observed values:
> >> >>>> http://www.stata.com/support/faqs/statistics/r-squared/
> >> >>>> but are you sure your -ivtobit- model is justified?  What is the
> >> >>>> process that results in observations being censored?  I suspect you
> >> >>>> have a lower limit at zero which is actually a very low conditional
> >> >>>> mean rounded down to zero--am I right?  You may be better off with a
> >> >>>> -gmm- model.
> >> >>>>
> >> >>>> On Sat, Sep 22, 2012 at 5:33 PM, Anat (Manes) Tchetchik
> >> >>>> <anatmanes@gmail.com> wrote:
> >> >>>> > Dear statalisters,
> >> >>>> >
> >> >>>> > I wonder if anyone knows any goodness of fit that is appropriate for
> >> >>>> > tobit with endogenous
> >> >>>> > variables (ivtobit). Not as in "regular" tobit, stata does not report any
> >> >>>> > goodness of fit measure, any idea how to estimate such a measure?
> >> >>>> > Any response will be greatly appreciated..
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--
Anat Tchetchik, PhD
Department of Hotel and Tourism Management
Guilford Glazer Faculty of Business and Management
Ben-Gurion University of the Negev
P.O.Box: 653
Beer-Sheva, Israel, 84105

E-mail:       anat@som.bgu.ac.il
Phone         972-(0)8-6479735
Fax:           972-(0)8-6472920
Web:          http://cmsprod.bgu.ac.il/Eng/som/hotelmanage/Staff/Academic/ChechikA.htm
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