Well the temperature in Milan is around 37 today. That's why I see more "a" than necessary! Sorry Marteen for my English-isation of your name!
Cheers,
Emanuele
> Date: Sat, 2 Aug 2008 13:44:21 +0200
> From: [email protected]
> To: [email protected]
> Subject: RE: st: a simple panel data question: FE and RE
>
> Maybe one day, email will come with pictures: then statalisters will
> be able to distinguish between Maarten and Martin :-)
>
> Quoting emanuele canegrati :
>
>>
>> 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: [email protected]
>>> Subject: Re: st: a simple panel data question: FE and RE
>>> To: [email protected]
>>>
>>> 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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>>> *
>>> * For searches and help try:
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>>> * http://www.ats.ucla.edu/stat/stata/
>>
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>>
>
>
>
>
> *
> * For searches and help try:
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> * http://www.ats.ucla.edu/stat/stata/
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