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Re: st: RE: RE: AW: correlate lag variables


From   Julia <j.statalist@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: RE: AW: correlate lag variables
Date   Tue, 11 May 2010 13:40:04 +0200

Dear Martin,

you are right, I should thank Peter for his help as well. I apologize
for not acknowledging this.

Best wishes,

Julia

On Tue, May 11, 2010 at 1:05 PM, Martin Weiss <martin.weiss1@gmx.de> wrote:
>
> <>
>
>
> "...as the two of you have explained."
>
>
> The credit for this statement should go to Peter and Nick, though, who made
> almost identical comments to this effect. My code did not specifically
> address this point.
>
>
>
> HTH
> Martin
>
> -----Ursprüngliche Nachricht-----
> Von: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Julia
> Gesendet: Dienstag, 11. Mai 2010 12:51
> An: statalist@hsphsun2.harvard.edu
> Betreff: Re: st: RE: RE: AW: correlate lag variables
>
> Dear Nick and Martin,
>
> thank you for the usefull comments! Indeed I have a panel data, so the
> more lags I include in the correlate command, less data is being used,
> as the two of you have explained.
>
> Best regards,
>
> Julia
>
> On Mon, May 10, 2010 at 6:54 PM, Nick Cox <n.j.cox@durham.ac.uk> wrote:
>> Yes; and I conjectured that the loss of 40 obs reflected a use of panel
> data. The original poster has yet to respond.
>>
>> Nick
>> n.j.cox@durham.ac.uk
>>
>> Lachenbruch, Peter
>>
>> I'm coning into this a little late, but did anyone notice that when you
> include lag 2 you have 225 observations and when you include only lag 1, you
> have 265.  Setting es=e(sample) after the lag 2 analysis and rerunning the
> correlation for lag 1 if es==1 might shed some light on the problem.
>>
>> Martin Weiss
>>
>> Try -pwcorr- instead:
>>
>> *************
>> clear*
>> set obs 100
>> gen y=1
>> replace y =.6*y[_n-1]+rnormal() in 2/l
>> gen byte time=_n
>> tsset time
>> corr y L.y L2.y
>> pwcorr y L.y
>> pwcorr y L.y L2.y
>> *************
>>
>> Julia
>>
>> I would like to calculate the correlation between a variable and its
>> past values. Thus, I use the following command:
>>
>> . correlate BI L1.BI L2.BI
>> (obs=225)
>>
>>              |           L.      L2.
>>              | BI       BI      BI
>> -------------+---------------------------
>>           BI|
>>          --. |   1.0000
>>          L1. |   0.0111   1.0000
>>          L2. |   0.0647   0.0161   1.0000
>>
>>
>> However, if I only ask the correlation for the first lag, my result
>> differs....
>>
>> . correlate BI L1.BI
>> (obs=265)
>>
>>              |             L.
>>              |    BI     BI
>> -------------+------------------
>>           BI|
>>          --. |   1.0000
>>          L1. |   0.0174   1.0000
>>
>>  Why does excluding the second lag affect the correlation between the
>> variable and its first lag?
>>
>> *
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