HOW INFORMATIVE ARE IN-SAMPLE INFORMATION CRITERIA TO FORECASTING?: THE CASE OF CHILEAN GDP
This paper compares out-of-sample performance, using the Chilean GDP dataset, of a large number of autoregressive integrated moving average (ARIMA) models with some variations to identify how to achieve the smallest root mean squared forecast error with models based on information criteria-Akaike, S...
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| Autor principal: | |
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| Lenguaje: | English |
| Publicado: |
Pontificia Universidad Católica de Chile. Instituto de Economía.
2013
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| Acceso en línea: | http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0719-04332013000100005 |
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