Without referring to your notes (quant)...... values, reason, testing, equation, referring

parott

True or false? You can use a DW statistic for testing for serial correlation on a regression equation that uses lagged values. Include a reason in your answer.

YAhmed

You use DW for serial correlation.

But the question shows that it is a time series model which by itself mostly has serial correlation. You will need to check the significance of auto correlations to determine whether it is a problem or not.

comp_sci_kid

small correction: JP
"
1 - ((n-1/n-k-1) - (1-r^2))

think that's it
"
1 - ((n-1)/(n-k-1) * (1-r^2))

Not Minus but *

CP



Edited 1 time(s). Last edit at Monday, May 3, 2010 at 07:03AM by cpk123.

CPATrader

True or false? You can use a DW statistic for testing for serial correlation on a regression equation that uses lagged values. Include a reason in your answer.

why is this false?
DW is used for serial correlation, isnt it?

bdavi77962

1 - ((n-1/n-k-1) - (1-r^2))

think that's it

genuinecfa

it is 1/sqrt(T) where T = # of observations of the sample. (# of time periods for which the time series regression is being performed).

CP

defour44

davidyoung@sitkapacific.com Wrote:
-------------------------------------------------------
> Nice, well done. I don't think we need to memorize
> it either, but I just did.
>
> Here's another one, which I'm sure you'll get.
>
> What is the standard error of the autocorrelations
> of the residuals? (it's not a big equation either)


Square root of n, where n is the number of lagged variables.

NO EXCUSES

yalo

Thanks CP and i will search for mvwt9 post.

President1988

actually do not think there is a need. I do not have trouble remembering the formula.

Breaking it down into 3 parts
1
1/n
(Xi-XBar)^2/(n-1)*Sx^2

helps me keep it in mind.

if you referred to searched for Level II posts by mvwt9 -> he had used the Confidence interval method - and selected the one slightly higher... or something like that, instead of memorizing this formula.

CP

neil1234

sf^2 = see^2 * {1+1/n + (Xi-xBar)^2/(n-1)sx^2}

CP