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RE: st: RE: RE: comparing different means using ttest
From
DE SOUZA Eric <[email protected]>
To
"[email protected]" <[email protected]>
Subject
RE: st: RE: RE: comparing different means using ttest
Date
Fri, 17 Dec 2010 15:27:26 +0100
I agree with you completely. And I drum it into my students. Which is why I organise my introductory econometrics course according to data structure: cross-section data, time series data and panel data, emphasising the assumptions required, the consequences of their non-realisation and possible remedies.
Eric
Eric de Souza
College of Europe
BE-8000 Brugge
Belgium
-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of Nick Cox
Sent: vrijdag 17 december 2010 14:33
To: '[email protected]'
Subject: RE: st: RE: RE: comparing different means using ttest
I'd leave economists to discuss that one. My larger point remains that applying tests that ignore time series structure to data that are time series is a dubious and dangerous thing to do.
Nick
[email protected]
DE SOUZA Eric
" The regression still assumes independent error terms."
True. But GDP does often behave as a random walk (with structural breaks, may be). Hence the errror terms are very likely to be uncorrelated.
One could also robustify against serial correlation in the error terms.
Nick Cox
The regression still assumes independent error terms. There is more scope for doing something about that in a regression framework then within -ttest-, but in terms of what Eric suggested it is still a matter of six on one side and half-a-dozen on the other.
DE SOUZA Eric
It does, because it simply avoids the starting point of David Lempert which in my opinion is a false start: regressing GDP levels on a time trend will get you nowhere. If David is interested testing the equality of GDP growth rates across two time periods, you pool the data, calculate the GDP growth rate and regress this variable on two dummy (binary) variables for each time period. In order to avoid perfect collinearit you drop one of the two dummies and test whether the coefficient on the other is equal to zero.
Steven Samuels
But. Eric, I don't think that pooling will solve the dependence issues that Nick mentioned.
On Dec 16, 2010, at 1:26 PM, DE SOUZA Eric wrote:
Why not just pool your data and regress %GDP-growth on a dummy
(binary) variable (and a constant, of course) which takes the value of one for one of the two sub-samples and zero for the other; and test whether the coefficient on the dummy is significantly different from zero (or examine its confidence interval) ?
You can robustify for heteroscedasticity.
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