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  help for ^icomp^               Stas Kolenikov, skolenik@@recep.glasnet.ru
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 Information criteria
 --------------------

          ^icomp^, [^rss^]


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

^icomp^ calculates, displays, and stores in ^r()^ some popular information
criteria, namely, Akaike information criteria (AIC), Schwartz Bayesian
information criteria (SBC, SBIC), and Bozdogan's index of informational
complexity (ICOMP), after an estimation command able to produce likelihood.
These criteria are used to select the ``best'' model compromising an
adequate goodness of fit and a small number of parameters by adding a
penalty for overparametrization to the lack of fit measure (estimate of the
maximum likelihood or the residual sum of squares). The best model, then,
minimizes the criterion. The three informational measures differ in the
penalty term, SBIC penalizing more severely for larger samples, and ICOMP
accounting for covariance structure of a model (and, thus, for
collinearity between the factors and dependence among the parameter
estimates).


 Options
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^rss^ specifies that the criteria are to be calculated from the residual
        sum of squares, rather that directly from the likelihood function
        (if the latter is unavailable).


 Author
 ------

Stas Kolenikov, skolenik@@[recep.glasnet.ru, nes.cemi.rssi.ru, yahoo.com]


 See also
 ---------

On-line: help for @est@, @postest@, @arimafit@ (if installed), @fitstat@ (if installed)
 Manual: ^[U] 23 Estimation and post-estimation commands^



 References
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Akaike, H. Information theory and an extension of the maximum likelihood
   principle. In B.N.Petrov and F.Csaki (eds), Second International
   Symposium on Information Theory, Academiai Kiado, Budapest, 267--281
   (1973).

Bozdogan, H. On the information-based measure of covariance complexity and
   its application to the evaluation of multivariate linear models.
   Communications in Statistics, Theory and Methods, 19(1), 221--278
   (1990).

Bozdogan, H. Empirical econometric modelling of food consumption using a
   new informational complexity approach. J. of Applied Econometrics, 12,
   563--592 (1997).

Kulback, S. and R. A. Leibler. On information and sufficiency. Annals of
   Mathematical Statistics, 22, 79--86 (1951).

Schwartz, G. Estimating the dimension of a model. Annals of Statistics, 6,
   461--464 (1978).
