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

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: MEDEL,CARLOS A
Lenguaje:English
Publicado: Pontificia Universidad Católica de Chile. Instituto de Economía. 2013
Materias:
Data mining
forecasting
ARIMA
seasonal adjustment
Easter effect
Acceso en línea:http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0719-04332013000100005
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spelling oai:scielo:S0719-043320130001000052013-09-24HOW INFORMATIVE ARE IN-SAMPLE INFORMATION CRITERIA TO FORECASTING?: THE CASE OF CHILEAN GDPMEDEL,CARLOS A Data mining forecasting ARIMA seasonal adjustment Easter effect 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.info:eu-repo/semantics/openAccessPontificia Universidad Católica de Chile. Instituto de Economía.Latin american journal of economics v.50 n.1 20132013-05-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0719-04332013000100005en10.7764/LAJE.50.1.135
institution Scielo Chile
collection Scielo Chile
language English
topic Data mining
forecasting
ARIMA
seasonal adjustment
Easter effect
spellingShingle Data mining
forecasting
ARIMA
seasonal adjustment
Easter effect
MEDEL,CARLOS A
HOW INFORMATIVE ARE IN-SAMPLE INFORMATION CRITERIA TO FORECASTING?: THE CASE OF CHILEAN GDP
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.
author MEDEL,CARLOS A
author_facet MEDEL,CARLOS A
author_sort MEDEL,CARLOS A
title HOW INFORMATIVE ARE IN-SAMPLE INFORMATION CRITERIA TO FORECASTING?: THE CASE OF CHILEAN GDP
title_short HOW INFORMATIVE ARE IN-SAMPLE INFORMATION CRITERIA TO FORECASTING?: THE CASE OF CHILEAN GDP
title_full HOW INFORMATIVE ARE IN-SAMPLE INFORMATION CRITERIA TO FORECASTING?: THE CASE OF CHILEAN GDP
title_fullStr HOW INFORMATIVE ARE IN-SAMPLE INFORMATION CRITERIA TO FORECASTING?: THE CASE OF CHILEAN GDP
title_full_unstemmed HOW INFORMATIVE ARE IN-SAMPLE INFORMATION CRITERIA TO FORECASTING?: THE CASE OF CHILEAN GDP
title_sort how informative are in-sample information criteria to forecasting?: the case of chilean gdp
publisher Pontificia Universidad Católica de Chile. Instituto de Economía.
publishDate 2013
url http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0719-04332013000100005
work_keys_str_mv AT medelcarlosa howinformativeareinsampleinformationcriteriatoforecastingthecaseofchileangdp
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