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Does BIC Estimate and Forecast Better than AIC?


  • Medel, Carlos A.
  • Salgado, Sergio C.


We test two questions: (i) Is the Bayesian Information Criterion (BIC) more parsimonious than Akaike Information Criterion (AIC)?, and (ii) Is BIC better than AIC for forecasting purposes? By using simulated data, we provide statistical inference of both hypotheses individually and then jointly with a multiple hypotheses testing procedure to control better for type-I error. Both testing procedures deliver the same result: The BIC shows an in- and out-of-sample superiority over AIC only in a long-sample context.

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  • Medel, Carlos A. & Salgado, Sergio C., 2012. "Does BIC Estimate and Forecast Better than AIC?," MPRA Paper 42235, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:42235

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    More about this item


    AIC; BIC; time-series models; overfitting; forecast comparison; joint hypothesis testing;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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