Does BIC Estimate and Forecast Better than AIC?
AbstractWe 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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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 42235.
Date of creation: 25 Oct 2012
Date of revision:
AIC; BIC; time-series models; overfitting; forecast comparison; joint hypothesis testing;
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-2012-11-11 (All new papers)
- NEP-ECM-2012-11-11 (Econometrics)
- NEP-ETS-2012-11-11 (Econometric Time Series)
- NEP-FOR-2012-11-11 (Forecasting)
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