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From |
Martin Weiss <martin.weiss@uni-tuebingen.de> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Sat, 2 Aug 2008 13:44:21 +0200 |

Maybe one day, email will come with pictures: then statalisters will be able to distinguish between Maarten and Martin :-)

Quoting emanuele canegrati <emanuele.canegrati@hotmail.it>:

As Martin said the best way is to imagine a model written in the following fashion:

Y = X*Beta + Z*mu + v

where Z is the matrix of individual dummies of NTxT. So in your case you will obtain estimates of mu(1) and mu(2) coefficients. Individual specific effect are unobservable (you don't see them in the regression) and vary across individuals. If you neglet to consider fixed effects as in the previous expression you obtain a biased and inconsistent estimation of the relation between Y and X. With FE option STATA produces automatically the outcome of an F test which tests the joint significance of individual dummies. This can help you to better understand which is the actual relation between X and Y.

Hope this help.

Emanuele Canegrati

Date: Fri, 1 Aug 2008 23:03:56 +0100 From: maartenbuis@yahoo.co.uk Subject: Re: st: a simple panel data question: FE and RE To: statalist@hsphsun2.harvard.edu One way of thinking about fixed effects (in a linear model) is that a dummy is added for each unit (minus one reference unit). These dummies absorb all the observed and unobserved differences between the units, so it does take the across variation into account. However, you can no longer describe what a unit level variable, like the average value of X, does to Y. In a random effects model you can describe the effects of unit level variables, but at a price: you now have to make a number of assumptions you did not have to make with a fixed effects model. The assumption that people like least is the assumption that the random effect is uncorrelated with the observed variables. -- Maarten --- yjh jsh wrote:Dear all, I have a newbie question here. sorry for this. I have a panel data with variable Y and X for two units for example. unit year X Y 1 1991 1 20 1 1992 2 19 1 1993 3 21 2 1991 10 40 2 1992 11 40 2 1993 11 39 That is, there is a larger cross variation than within variation As i understand, FE only address the variation within units. So, if I use FE, i will not find a significant relationship between x and y based on the nature of the hypothectical data. but this finding does not take into account the fact Y takes higher vaue in unit 2 because x takes higher value in that unit. That is, fe failed to represent the across-variation. Is my understanding correct? Sorry for this simple question. best * * 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/

-----------------------------------------

Maarten L. Buis

Department of Social Research Methodology

Vrije Universiteit Amsterdam

Boelelaan 1081

1081 HV Amsterdam

The Netherlands

visiting address:

Buitenveldertselaan 3 (Metropolitan), room Z434

+31 20 5986715

http://home.fsw.vu.nl/m.buis/

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**Follow-Ups**:**RE: st: a simple panel data question: FE and RE***From:*emanuele canegrati <emanuele.canegrati@hotmail.it>

**References**:**st: a simple panel data question: FE and RE***From:*"yjh jsh" <jshyjh1@gmail.com>

**Re: st: a simple panel data question: FE and RE***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**RE: st: a simple panel data question: FE and RE***From:*emanuele canegrati <emanuele.canegrati@hotmail.it>

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