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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: RE: AW: correlate lag variables |

Date |
Mon, 10 May 2010 17:54:41 +0100 |

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

**Follow-Ups**:**Re: st: RE: RE: AW: correlate lag variables***From:*Julia <j.statalist@gmail.com>

**References**:**st: correlate lag variables***From:*Julia <j.statalist@gmail.com>

**st: AW: correlate lag variables***From:*"Martin Weiss" <martin.weiss1@gmx.de>

**st: RE: AW: correlate lag variables***From:*"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>

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