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From |
Paulo Regis <pauloregis.ar@googlemail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: AKAIKE formula |

Date |
Tue, 13 Apr 2010 17:14:54 +0800 |

Ok, i got the main point. Now, what if the issue involves nested models in a panel. Model A: Y = B X + e Then, we have the "nested" model B Y = B X +c Y(-1) + e if Y(-1) were any variable, no problem: the t-statistic. However, there is endogeneity and now i have that i need to use IV. That is the reason I wanted to use AIC. Actually, I made the two mdoels very simple. I add other variables in addition to Y(-1) so I should look at the F-statistic or a fit measure. Kind Regards Paulo On Tue, Apr 13, 2010 at 4:06 PM, Maarten buis <maartenbuis@yahoo.co.uk> wrote: > --- On Tue, 13/4/10, Paulo Regis wrote: >> I have a question about Akaike Info Criterion. Stata >> calculates aic (using "estac ic" after the regression >> command) with the formula: >> >> AIC = -2 * log (likelihood) + 2 * (k+1) ; k= >> number of parameters >> >> >> In the linear regression model, this is similar to use the >> formula: >> >> AIC = n*ln(RSS/n) +2*(k+1), RSS = residuals SS >> >> This was addressed before in this list by the following >> post: >> >> http://www.stata.com/statalist/archive/2003-09/msg00365.html >> >> However, my problem is that I want to compare OLS with IV >> models using AKAIKE. The command "estac ic" is not available >> for -ivreg. Can I compute the AIC by myself using the second >> formula? > > The logic behind this is that in a linear regression the > log likelihood is a function of the RSS. So, you would need > to argue that in -ivreg- the likelihood would need to derive > the likelihood of your model and show that it is a similar > function of the RSS. I haven't done so, but I am doubtful > that that is the case. > > Moreover, differences in fit statistic are not a good way of > choosing between an IV model and an non-IV model like -regress-. > The whole point of IV models, as I understand them, is that > you believe some of the association between a variable of > interest x and the dependent variable y is spurious, and you > use instrumental variables to throw away the spurious > association and (hopefully) keep the "real" association. A > fit statistic cannot distinguish between "real" and "spurious" > association, so a non-IV model should "fit" better because it > doesn't throw the spurious part of the association away. So, > differences in fit statistic cannot help you in choosing > between these models, at best they tell you how much > information is being thrown away by the IV method, but since > throwing away information is the whole point of IV methods > (because you have a theory that this information is "bad"), > that does not help much. > > -- Maarten > > -------------------------- > Maarten L. Buis > Institut fuer Soziologie > Universitaet Tuebingen > Wilhelmstrasse 36 > 72074 Tuebingen > Germany > > http://www.maartenbuis.nl > -------------------------- > > > > > * > * 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: AKAIKE formula***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**References**:**st: AKAIKE formula***From:*Paulo Regis <pauloregis.ar@googlemail.com>

**Re: st: AKAIKE formula***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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