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From |
Gaul� Patrick <[email protected]> |

To |
"[email protected]" <[email protected]> |

Subject |
RE : st: a simple panel data question: FE and RE |

Date |
Sat, 2 Aug 2008 16:45:32 +0200 |

It might be useful to do a variance decomposition to see how much of the variance is due to the cross-sectional dimension and how much is due to the time dimension. If much of the variance comes from the cross-sectional dimension, you could use the between estimator or simply pooled OLS with clustered standard errors. Patrick Gaul� ________________________________________ De : [email protected] [[email protected]] de la part de yjh jsh [[email protected]] Date d'envoi : samedi 2 ao�t 2008 14:20 � : [email protected] Objet : Re: st: a simple panel data question: FE and RE This does helps. thanks For me, it seems that different "levels" of Y in units are assumed to be caused by those unobserved time-invariant variables. What if they are not? In my hypothetic case, for example, for both unit 1 and 2, we can't see relationship between x and y if we only consider within-variation. But this conceals the fact that Y takes higher value in unit 2 when 2 takes higher value in unit 2. That is, we do see a relationship that higher x causes y. so, if FE a bad choice if we have a large between variation compared to within variation in the data? thanks * * 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: RE : st: a simple panel data question: FE and RE***From:*"yjh jsh" <[email protected]>

**References**:**st: a simple panel data question: FE and RE***From:*"yjh jsh" <[email protected]>

**Re: st: a simple panel data question: FE and RE***From:*Maarten buis <[email protected]>

**RE: st: a simple panel data question: FE and RE***From:*emanuele canegrati <[email protected]>

**Re: st: a simple panel data question: FE and RE***From:*"yjh jsh" <[email protected]>

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