[R] statistics - hypothesis testing question

[Pfaff, Bernhard Dr.]

Hello Mark,

in addition and complementing the already provided answers to your
question. You want to consider the J-test, too. For an outline and the
pitfalls of this test, see:

http://citeseer.ist.psu.edu/cache/papers/cs/24954/http:zSzzSzwww.econ.qu
eensu.cazSzfacultyzSzdavidsonzSzbj4-noam.pdf/bootstrap-j-tests-of.pdf


Best,
Bernhard 

>
>I estimate two competing simple regression models, A and B 
>where the LHS
>is the same in both cases but the predictor is different (
>I handle the intercept issue based on other postings I have seen ). I
>estimate the two models on a weekly basis over 24 weeks. 
>So, I end up with 24 RSquaredAs and 24 RsquaredBs, so essentally 2 time
>series of Rsquareds. This doesn't have to be necessarily 
>thought of as a
>time series problem but, is there a usual way, given the Rsquared data,
>to test 
>
>H0 : Rsquared B = Rsquared A versus H1 : Rsquared B > Rsquared A 
>
>so that I can map the 24 R squared numbers into 1 statistic. Maybe
>that's somehow equivalent to just running 2 big regressions over the
>whole 24 weeks and then calculating a statistic from those based on
>those regressions ?
>
>I broke things up into 24 weeks because I was thinking that the
>stability of the performance difference of the two models could be 
>examined over time. Essentially these are simple time series 
>regressions
>X_t = B*X_t-1 + epsilon so I always need to consider
>whether any type of behavior is stable.  But now I am thinking 
>that,  if
>I just want one overall number,  then maybe I should be considering all
>the data simultaneously ? 
>
>In a nutshell,  I am looking for any suggestions on the best 
>way to test
>whether Model B is better than Model A where
>
>Model A :  X_t = Beta*X_t-1 + epsilon
>
>Model B :  X_t = Betastar*Xstar_t-1 + epsilonstar
>
>
>Thanks fo your help.
>--------------------------------------------------------
>
>This is not an offer (or solicitation of an offer) to 
>buy/se...{{dropped}}
>
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