How Informative are In-sample Information Criteria to Forecasting? The Case of Chilean GDP



Document title: How Informative are In-sample Information Criteria to Forecasting? The Case of Chilean GDP
Journal: Latin american journal of economics
Database: CLASE
System number: 000399149
ISSN: 0719-0425
Authors: 1
Institutions: 1Banco Central de Chile, Departamento de Investigaciones Económicas, Santiago de Chile. Chile
Year:
Season: May
Volumen: 50
Number: 1
Pages: 133-161
Country: Chile
Language: Inglés
Document type: Artículo
Approach: Aplicado
English abstract 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 ef fect. 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 ef fect improves forecast accuracy for actual and seasonally adjusted data
Disciplines: Economía
Keyword: Econometría,
Condiciones económicas,
Datos estadísticos,
Predicción,
Ajuste estacional,
Chile
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