At 08:39 PM 6/23/2009, kokootchke wrote:
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...
Here is a simple example that shows the sensitivity of BIC and AIC to
sample size:
. sysuse auto, clear
(1978 Automobile Data)
. quietly reg price mpg trunk weight
. estat ic
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 74 -695.7129 -682.6073 4 1373.215 1382.431
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note
. expand 2
(74 observations created)
. quietly reg price mpg trunk weight
. estat ic
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 148 -1391.426 -1365.215 4 2738.429 2750.418
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note
So, even if data are missing at random with your X variable, the
smaller sample sizes that result from its inclusion will drive down
the BIC and AIC stats quite a bit.
-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME: (574)289-5227
EMAIL: [email protected]
WWW: http://www.nd.edu/~rwilliam
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