Medel, Carlos A. and Salgado, Sergio C. (2012): Does BIC Estimate and Forecast Better than AIC?

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Abstract
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 typeI error. Both testing procedures deliver the same result: The BIC shows an in and outofsample superiority over AIC only in a longsample context.
Item Type:  MPRA Paper 

Original Title:  Does BIC Estimate and Forecast Better than AIC? 
Language:  English 
Keywords:  AIC; BIC; timeseries models; overfitting; forecast comparison; joint hypothesis testing 
Subjects:  C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C51  Model Construction and Estimation C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods ; Simulation Methods C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C22  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes 
Item ID:  42235 
Depositing User:  Carlos A. Medel 
Date Deposited:  04 Nov 2012 18:29 
Last Modified:  26 Sep 2019 10:44 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/42235 