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From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Thu, 31 Jan 2013 13:18:32 +0000 |

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 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: normality test using the over identifying moment conditions***From:*Usman Gilani <u.gilani@icloud.com>

**Re: st: normality test using the over identifying moment conditions***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: normality test using the over identifying moment conditions***From:*Usman Gilani <u.gilani@icloud.com>

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