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From |
Joerg Luedicke <[email protected]> |

To |
[email protected] |

Subject |
st: Re: st: Re: st: RE: Truncated sample or Heckman selection |

Date |
Thu, 4 Oct 2012 16:37:52 -0500 |

```
As Nick explained it already, if a variable is by definition bounded
between [0,1], then the values cannot be considered censored, neither
from below nor from above. If the authors you refer to used a Tobit
model for such data then yes, they probably did it wrong.
If these authors used the same data that you have, it might be
worthwhile posting a reference. As Nick already pointed out, it is
hard to imagine how you want to account for selection without any
information for non-innovative firms.
Joerg
On Thu, Oct 4, 2012 at 4:17 PM, Ebru Ozturk <[email protected]> wrote:
> But, the dependent variable includes many zero values as many firms do not produce innovation and of course no sales from this tyep of innovation. Also, published papers have used Tobit regression and Heckman two step correction with the same data, are they all wrong then?
>
> Ebru
>
> ----------------------------------------
>> Date: Thu, 4 Oct 2012 20:54:49 +0100
>> Subject: st: Re: st: RE: Truncated sample or Heckman selection
>> From: [email protected]
>> To: [email protected]
>>
>> I agree on #1.
>>
>> On #2, how is Ebru going to fit any kind of model with no data on predictors?
>>
>> Nick
>>
>> On Thu, Oct 4, 2012 at 7:50 PM, Millimet, Daniel <[email protected]> wrote:
>> > 1. A fractional logit model is more appropriate when modeling percentages.
>> > 2. The data set up is in between the usual Heckman vs. truncated model setup. With the typical Heckman approach, X's are observed for all observations and there is no information on the missing outcome. With a truncated setup, we observe no X's, but have information at least on the range of values for the outcome. Here, you do know something about the value of Y for the censored observations as in the truncated setup, but you only observe a subset of the X's. To me, it sounds you could perhaps "invent" a new model that is a zero-inflated fractional logit model, since you have 1 set of regressors that impact perhaps the probability of no innovation, and then a second set of regressors that impacts the amount of innovation conditional on this being positive.
>> >
>> > Anyway, perhaps not the best answer.
>> >
>> >
>> > -----Original Message-----
>> > From: [email protected] [mailto:[email protected]] On Behalf Of Ebru Ozturk
>> >
>> > I have a question that I cannot decide whether I should use truncated regression or Heckman sample selection.
>> > For instance, in the dataset, firms that produce any type of innovation (process or product) give information about other 'x' variables. In other words, firms that do not produce any innovation do not answer other questions as these questions are directly related to firms' innovation activities. So, the 'x' variables that I am interested in have no values only for those firms that do not produce innovation. But, I know the dependent (y) variable in both case, either firms produce innovation or not produce.
>> >
>> >
>> > I am planning to run tobit regression as the dependent variable is percentage between 0 - 100 and Heckman sample selection model to check selection bias. But, I can not decide whether it is truncated sample or Heckman sample selection.
>> >
>> > So, what do you think?
>> >
>> > Thank you very much, Ebru.
>>
>> *
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> *
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```

**References**:**st: Truncated sample or Heckman selection***From:*Ebru Ozturk <[email protected]>

**st: RE: Truncated sample or Heckman selection***From:*"Millimet, Daniel" <[email protected]>

**st: Re: st: RE: Truncated sample or Heckman selection***From:*Nick Cox <[email protected]>

**RE: st: Re: st: RE: Truncated sample or Heckman selection***From:*Ebru Ozturk <[email protected]>

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