HOW INFORMATIVE ARE IN-SAMPLE INFORMATION CRITERIA TO FORECASTING?: THE CASE OF CHILEAN GDP

dc.creator	MEDEL,CARLOS A
dc.date	2013-05-01
dc.date.accessioned	2019-04-25T12:42:00Z
dc.date.available	2019-04-25T12:42:00Z
dc.identifier	https://scielo.conicyt.cl/scielo.php?script=sci_arttext&pid=S0719-04332013000100005
dc.identifier.uri	http://revistaschilenas.uchile.cl/handle/2250/61281
dc.description	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, Schwarz, and Hannan-Quinn. The analysis also addresses the role of seasonal adjustment and the Easter effect. The results show that Akaike and Schwarz are better criteria for forecasting when using actual series and Schwarz and Hannan-Quinn are better with seasonally adjusted data. Accounting for the Easter effect improves forecast accuracy for actual and seasonally adjusted data.
dc.format	text/html
dc.language	en
dc.publisher	Pontificia Universidad Católica de Chile. Instituto de Economía.
dc.relation	10.7764/LAJE.50.1.135
dc.rights	info:eu-repo/semantics/openAccess
dc.source	Latin american journal of economics v.50 n.1 2013
dc.subject	Data mining
dc.subject	forecasting
dc.subject	ARIMA
dc.subject	seasonal adjustment
dc.subject	Easter effect
dc.title	HOW INFORMATIVE ARE IN-SAMPLE INFORMATION CRITERIA TO FORECASTING?: THE CASE OF CHILEAN GDP