Re: st: normality test using the over identifying moment conditions

[Nick Cox <njcoxstata@gmail.com>]

If you want a test for normality, the best I know is the
Doornik-Hansen test implemented in Stata.

It would have been helpful to explain your use of R in the first
instance, and it's still true that R users on this list might want to
know which package you were using. It's also true that you are likely
to get better help on R mailing lists, which you may be doing for all
I know. More pointedly, I'd say that questions centred on how to
interpret R results are out of place on this list, although statistics
questions are not.

Nick

On Thu, Jan 31, 2013 at 1:06 PM, Usman Gilani <u.gilani@icloud.com> wrote:
> Dear Nick,
>
> the dataset has 1000 obs.
> and I'm not using Stata this output is from R
> i tried to run this test in stata but cause of limited stata knowledge I couldn't do it.
>
> please tell me how can i do gmm test with following moment conditions in stata
>
> thanks
>
> best
>
> Gilani
> On 31 Jan 2013, at 12:30, Nick Cox <njcoxstata@gmail.com> wrote:
>
>> There is no mention here of what command you are using. With this kind
>> of data the number of values is usually so large that any test will
>> produce results significant at conventional levels, i.e. normality
>> will be rejected even for trivial deviations from normality.
>>
>> A search of the archives will show many posts explaining why tests of
>> normality are usually a bad idea.
>>
>> Nick
>>
>> On Thu, Jan 31, 2013 at 12:21 PM, Usman Gilani <u.gilani@icloud.com> wrote:
>>> Hi,
>>> I'm trying to interpret the following results, with respect to "normality
>>> test using the over identifying moment conditions"
>>>
>>> where returns have normal distribution
>>> with parameter mu,sd
>>> and i have 4 moment conditions
>>>
>>>> E[r-mu/sd]=0
>>>
>>>> E[(r-mu)^2/sd-1]=0
>>>
>>>> E[(r-mu)^3/sd^3]=0
>>>
>>>> E[(r-mu)^4/sd^4-3]=0
>>>
>>> output..
>>> gel(g = g, x = returns, tet0 = c(f3$estimate[1], f3$estimate[2]))
>>>
>>> Type of GEL:  EL
>>>
>>> Coefficients:
>>>              Estimate  Std. Error     t value   Pr(>|t|)
>>> mean  -0.01168   0.05614    -0.20805   0.83519
>>> sd         1.77591   0.03965    44.79218   0.00000
>>>
>>> Lambdas:
>>>                        Estimate   Std. Error  t value    Pr(>|t|)
>>> Lambda[1]   -0.09743    0.03912    -2.49028    0.01276
>>> Lambda[2]    0.65728    0.02443    26.90505    0.00000
>>> Lambda[3]    0.03247    0.01304     2.48961    0.01279
>>> Lambda[4]   -0.10954    0.00407   -26.90423    0.00000
>>>
>>> Over-identifying restrictions tests: degrees of freedom is 2
>>>                     statistics     p-value
>>> LR test   2.3341e+02   2.0730e-51
>>> LM test   7.2417e+02   5.5954e-158
>>> J test      7.2417e+02    5.5954e-158
>>>
>>> Convergence code for the coefficients:  0
>>>
>>> Convergence code for the lambdas:  0
>>>
>>>
>>> does the J-test p-value rejecting the null E[g(theta,x)]=0, and which moment
>>> condition is true under normality
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