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Re: st: robust substitute for the tobit model
Thanks. I was curious to know whether the word
"significantly" was just fluff on a rhetorical claim or grew
out of some formal test. You've answered that.
Nick
[email protected]
nicola.baldini
I just had a look at the abstract. Reading more deeply the paper I found
that authors used symmetrically trimmed least squares (STLS) estimator
proposed by Powell (1986). To compare the parametric and semi-parametric
estimations, they used Hausmans type specification test proposed in
Newey (1987).
Sorry, but it is definitevely more a practical application of existing
statistics than the development of new ones. I can provide you with the
paper - obviously off the list - if you still find it interesting.
<references in original>
Nick Cox
>I don't have access to this journal. What significance
>procedure justifies their claim?
[email protected]
>> I want just let you know something related to this issue
>> (althought no Stata code is provided):
>>
>> Quote from the abstract: "this paper attempts to apply
>> symmetrically trimmed least squares estimation as a
>> semi-parametric estimation of the Tobit model in order to
>> model firms' R&D expenditures with zero values. The result of
>> specification test indicates the semi-parametric estimation
>> outperforms the parametric ML estimation significantly."
>>
>> Full reference:
>> Scientometrics, Vol. 69, No. 1 (2006) 57�67
>> A semi-parametric modeling of firms' R&D expenditures with zero values
>> SEUNG-HOON YOO & HYE-SEON MOON
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