Does BIC Estimate and Forecast Better Than AIC?
AbstractWe test two questions: (i) Is the Bayesian information criterion (BIC) more parsimonious than the Akaike information criterion (AIC)?, and (ii) Can the BIC forecast better than the AIC? 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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Bibliographic InfoPaper provided by Central Bank of Chile in its series Working Papers Central Bank of Chile with number 679.
Date of creation: Nov 2012
Date of revision:
Other versions of this item:
- 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-02-08 (All new papers)
- NEP-ETS-2013-02-08 (Econometric Time Series)
- NEP-FOR-2013-02-08 (Forecasting)
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