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From |
kokootchke <kokootchke@hotmail.com> |

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

Subject |
RE: st: Model selection using AIC/BIC and other information criteria |

Date |
Tue, 23 Jun 2009 21:39:59 -0400 |

Thank you, Richard. This was exactly what I thought... but I remember from my metrics classes long time ago that both AIC and BIC depend on N (sample size)... and I confirmed this by simply looking at these wikipedia entries... but, just like you, I also feared that, even though both criteria adjust for the sample size, maybe you can't compare between AICs and BICs when the models use different # of observations... Anyway, I just wanted to make sure I wasn't missing something else... Thanks a lot!! Adrian ---------------------------------------- > Date: Tue, 23 Jun 2009 21:20:28 -0500 > To: statalist@hsphsun2.harvard.edu; statalist@hsphsun2.harvard.edu > From: Richard.A.Williams.5@ND.edu > Subject: Re: st: Model selection using AIC/BIC and other information criteria > > At 06:07 PM 6/23/2009, kokootchke wrote: >>Dear all, >> >>I have a model that says that the return or yield spread of a bond >>issued by a country depends non-linearly on the country's >>probability of default. If I assume that this probability of default >>follows a logistic form, I get that the log spread depends linearly >>on "stuff" which I take to be macroeconomic variables. To choose the >>best model, I use AIC/BIC. >> >>One interesting fact I observe is that in some cases, I see that >>both AIC and BIC select a model that contains some variable X even >>when a lot of data points are missing for that particular variable, >>which means I actually lose a lot of observations when I include >>such variable X. >> >>More specifically, I have: >> >>MODEL 1 >> >>regress log_spread a b c X >>estat ic >> >>which gives AIC = 915 >> >>then, >> >>MODEL 2 >> >>regress log_spread a b c >>estat ic >> >>which gives AIC = 1500 >> >>but the OLS in model 1 uses 1200 observations while the OLS in model >>2 uses 2800 observations (because 1600 observations are missing in >>variable X)!! >> >>You would think that this would be because X is very relevant to >>explain the spread, but in fact I see some cases when this variable >>is statistically insignificant!! > > Somebody can correct me if I am wrong, but I don't think it is legit > to compare BIC and AIC statistics that have been estimated on > different samples. I don't think these stats are totally immune to > differences in sample size -- and even if they were the two samples > used might be very different, e.g. maybe those 1600 missing cases are > all bonds from the US. > > I'm guessing a fairer comparison would be > > nestreg, lr: reg log_spread (a b c) X > > The same sample will be used for both regressions and you will get > BIC and AIC stats at the end. > > I think your bigger concern, though, is losing more than half your > cases when you include X. You need to find out why those data are > missing and then decide what to do about it. > > > ------------------------------------------- > Richard Williams, Notre Dame Dept of Sociology > OFFICE: (574)631-6668, (574)631-6463 > HOME: (574)289-5227 > EMAIL: Richard.A.Williams.5@ND.Edu > WWW: http://www.nd.edu/~rwilliam > > * > * 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/ _________________________________________________________________ Insert movie times and more without leaving Hotmail®. http://windowslive.com/Tutorial/Hotmail/QuickAdd?ocid=TXT_TAGLM_WL_HM_Tutorial_QuickAdd_062009 * * 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: Model selection using AIC/BIC and other information criteria***From:*Richard Williams <Richard.A.Williams.5@ND.edu>

**References**:**st: Analyze a subpopulation of survey data in Stata 10.1***From:*"Karadogan, Figen" <fo145502@ohio.edu>

**st: Model selection using AIC/BIC and other information criteria***From:*kokootchke <kokootchke@hotmail.com>

**Re: st: Model selection using AIC/BIC and other information criteria***From:*Richard Williams <Richard.A.Williams.5@ND.edu>

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