I have a question and it's only relation to R is that I probably need R
after I understand what to do.
 
Both models are delta y_t = Beta +  epslion 
and suppose I have a null hypothesis and alternative hypothesis
 
 
H_0 :   delta y_t = zero    + epsilon            epsilon is normal ( 0,
sigmazero^2 )
 
 
H_1     delta y_t = beta   + epsilon              epsilon is normal (
sigmabeta^2 )
 
 
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------
 
so, i calculate the MLE's under the null and the alternative as :
 
under H_0               beta hat = 0     and   sigmazero^2 hat = sum
over t ( delta y_t - zero )^2/ (n-1)
 
 
under H_1               beta hat =  ( sum of delta y_t ) /n    and
sigmabeta^2 = sum over t ( delta y_t - beta hat )^2/(n-1)
 
------------------------------------------------------------------------
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what i have blanked out on is how i take the estimates above and test
which model is more likely given the data ?
I think I used to know this so I apologize if this is a stupid question.
I used to take my estimates and th use dnorm or pnorm or one of those
but
I can't remember what I did and I can't find my old code.  thanks.
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