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From |
DE SOUZA Eric <eric.de_souza@coleurope.eu> |

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

Subject |
RE: st: RE: RE: comparing different means using ttest |

Date |
Fri, 17 Dec 2010 10:06:33 +0100 |

" 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. Eric Eric de Souza College of Europe BE-8000 Brugge (Bruges) Belgium -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nick Cox Sent: 17 December 2010 09:57 To: 'statalist@hsphsun2.harvard.edu' Subject: RE: st: RE: RE: comparing different means using ttest 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. Nick n.j.cox@durham.ac.uk 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. * * 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/ * * 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: comparing different means using ttest***From:*Nick Cox <n.j.cox@durham.ac.uk>

**References**:**st: comparing different means using ttest***From:*David Lempert <david.lempert@gmail.com>

**st: RE: comparing different means using ttest***From:*Nick Cox <n.j.cox@durham.ac.uk>

**st: RE: RE: comparing different means using ttest***From:*Nick Cox <n.j.cox@durham.ac.uk>

**RE: <POSSIBLE SPAM>st: RE: RE: comparing different means using ttest***From:*DE SOUZA Eric <eric.de_souza@coleurope.eu>

**Re: <POSSIBLE SPAM>st: RE: RE: comparing different means using ttest***From:*Steven Samuels <sjsamuels@gmail.com>

**RE: st: RE: RE: comparing different means using ttest***From:*DE SOUZA Eric <eric.de_souza@coleurope.eu>

**RE: st: RE: RE: comparing different means using ttest***From:*Nick Cox <n.j.cox@durham.ac.uk>

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