```.-
help for ^bicdrop1^ - 1.0 - 6 Mar 2005
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Estimate the probability a model is more likely without each explanatory variab
> le
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^bicdrop1^ [^, h^ighlight^(name)^ ]

Description
-----------

^bicdrop1^ is a post-estimation command that uses the Bayesian Information Crit
> erion
(BIC) to estimate the probability that the model would be more likely after dro
> pping
one of the explanatory variables.  The BIC was first proposed by Schwarz (1978)
>  and
further developed by Raftery (1995).

It works after the following estimation commands:
^regress^, ^logistic^, ^logit^, ^ologit^, ^oprobit^, ^mlogit^, ^poisson^, ^nbre
> g^.

It also reports Akaike's AIC, an earlier measure of model likelihood, and BIC'
> (BIC
prime), an alternative measure proposed by Raftery for model comparison.

The command drops each explanatory variable from the model and reports the AIC,
>  BIC
and BIC' associated with the resulting nested model and uses the differences be
> tween
the BIC for the reduced model and the full (original) model to calculate a
probability that the model is less likely if that explanatory (or independent)
variable is removed.

Note that the BIC difference is not a traditional hypothesis test, but a compar
> ison
between the likelihood of two models: the original model and the model without
> one
of the variables.  Nevertheless, the BIC difference is a more rigourous test of
>
whether the "true" model (i.e. the most likely model, given the data and the
likelihood form) contains the variable in question, especially where the tradit
> ional
significance tests are weak: where N is large; or where there are a lot of
explanatory variables (where k is large).

Acknowledgements
----------------
This program was based on the approach taken by the command lrdrop1 (developed
> by
Z. Wang)  and was suggested by Richard Williams of the University of Notre Dame
> .
The author is grateful for assistance and encouragement from Richard Williams o
> f
the University of Notre Dame in the testing of this routine.

Options
-------
^h^ighlight(^"colour"^) highlights variables which are likely to be part of t
> he model.
"colour" can be any of "^w^hite" "^g^reen" "^y^ellow" "^r^ed" or "^s^peci
> al"

Examples
--------
. ^regress cbecs09 cprcs03 cprcs05 ^
. ^bicdrop1^

Author: Paul Millar
www.ucalgary.ca/~pemillar/stata.htm
pemillar@@ucalgary.ca