Proyección Agregada y Desagregada del PIB Chileno con Procedimientos Automatizados de Series de Tiempo
This paper compares the out-of-sample error of two forecasting methods for Chile’s GDP. The first method forecasts the aggregate GDP, while the second aggregates the forecasts of the supply-side components. As forecasting method we use the automatic model selection contained in the seasonal adjustment program X12-ARIMA version 0.2.10. Our sample includes the information contained in the first data release until 2009.I. For the whole sample, this paper finds no significant difference between both methods, according to tests commonly used in this literature. However, in periods of low GDP volatility the aggregate method performs better in terms of root mean squared error, while the disaggregate approach outperforms the aggregate one in periods of high GDP volatility.
|Date of creation:||May 2010|
|Contact details of provider:|| Postal: Casilla No967, Santiago|
Phone: (562) 670 2000
Fax: (562) 698 4847
Web page: http://www.bcentral.cl/
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Marcus Cobb, 2009. "Forecasting Chilean Inflation From Disaggregate Components," Working Papers Central Bank of Chile 545, Central Bank of Chile.
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 20(1), pages 134-144, January.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-263, July.
- Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809.