Ok, I see, the 20% cannot be the right p- value. Does someone use Stata to check what the
newest xtabond2 says?
U seem to have some experience with dpanel estimation. What do u think about that: If I
have a 12x412 panel is it then ok to use 225 instruments or should one stay near 100
instruments?
I ask this because the 'Sargan' test p value stays for few instruments around
0,00. Could this point to an omitted variable bias?
Cheers
Leon
Am 22.05.2013 um 19:23 schrieb Rodrigo Alfaro Arancibia <ralfaro(a)fen.uchile.cl>:
Sure, there is some difference between Gretl and Stata's
user-written ado. I think that the problem is how time-dummies are used as instruments or
so. But my point is the following: you have 3 chi2(100) tests at hand, which are
well-above 100, and therefore it rejects strongly the null. I don't see how you could
have a p-value of 20%!!
2013/5/20 Pindar <pindar777(a)gmail.com>
> Am 20.05.2013 11:56, schrieb Sven Schreiber:
>> Am 20.05.2013 11:52, schrieb Pindar:
>>
>>> Hola Rodrigo,
>>>
>>> The p-value for Hansen test is reported as " 0.218".
>>> But with the output in the paper and gretl there are 3 different test
>>> statistics for chi2(100):
>>>
>>> Sargan_xtabond2: 186.90
>>> Sargan_gretl: 154.81
>>> Hansen_xtabond2: 110.70
>>>
>>> I would like to be sure how to interpret differences in the diagnostic
>>> checks between gretl and stata.
>>>
>> Yes, a useful question I think. But are the coeff estimates always the
>> same, are you absolutely sure you are comparing identical
>> specificiations? In panel settings and GMM settings there can be subtle
>> differences.
>
> Thanks Sven for pointing me to the 'always': The coefficients for the const
and the time dummies differ!
>
> Trying to change the setting for the time dummies leads to 'completely
different' coefficients while
> it does not alter the Sargan test statistic. I obviously failed in replicating the
time dummy instruments:
>
> <hansl>
> open abdata.gdt
> genr time
> genr timedum
> list TD_roodman = dt_2 dt_3 dt_4 dt_5 dt_6 dt_7 dt_8
>
> dpanel 1; n const w w(-1) k k(-1) TD_roodman ; \
> GMM(n,2,8) GMM(w,2,8) GMM(k,2,8) \
> GMMlevel(w,1,1) GMMlevel(k,1,1) TD_roodman --sys
>
> # This estimation gives a 'Sargan test'
> # Sargan over-identification test: Chi-square(100) = 154.367 [0.0004]
> <hansl>
>
> Best
> Leon
>
> Here the Roodman stata output of coefficients:
> <djfegaef.png>
>
>
>> cheers,
>> sven
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>
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