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Informational Complexity Criteria For Regression Models

Author

Listed:
  • Bozdogan, H.
  • Haughton, D.

Abstract

This paper pursues three objectives in the context of multiple regression models: 1) To give a rationale for model selection criteria which combine a badness of fit term (such as minus twice the log likelihood) with a measure of complexity of a model. 2) To investigate the asymptotic consistency properties of the class of ICOMP criteria first in the case when one of the models considered is the true model and to introduce and establish a consistency property for the case when none of the models is the true model. 3) To investigate the finite sample behavior of ICOMP criteria by means of a simulation study where none of the models considered is the true model.

Suggested Citation

  • Bozdogan, H. & Haughton, D., 1996. "Informational Complexity Criteria For Regression Models," Papers 96.414, Toulouse - GREMAQ.
    • Handle: RePEc:fth:gremaq:96.414
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    Keywords

    ECONOMETRICS;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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