EconPapers    
Economics at your fingertips  
 

Traditional versus unobserved components methods to forecast quarterly national account aggregates

Gustavo Marrero ()

Journal of Forecasting, 2007, vol. 26, issue 2, 129-153

Abstract: We aim to assess the ability of two alternative forecasting procedures to predict quarterly national account (QNA) aggregates. The application of Box-Jenkins techniques to observed data constitutes the basis of traditional ARIMA and transfer function methods (BJ methods). The alternative procedure exploits the information of unobserved high- and low-frequency components of time series (UC methods). An informal examination of empirical evidence suggests that the relationships between QNA aggregates and coincident indicators are often clearly different for diverse frequencies. Under these circumstances, a Monte Carlo experiment shows that UC methods significantly improve the forecasting accuracy of BJ procedures if coincident indicators play an important role in such predictions. Otherwise (i.e., under univariate procedures), BJ methods tend to be more accurate than the UC alternative, although the differences are small. We illustrate these findings with several applications from the Spanish economy with regard to industrial production, private consumption, business investment and exports. Copyright © 2007 John Wiley & Sons, Ltd.

Date: 2007
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed

Downloads: (external link)
http://hdl.handle.net/10.1002/for.1015 Link to full text; subscription required (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:26:y:2007:i:2:p:129-153

Access Statistics for this article

Journal of Forecasting is currently edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley-Blackwell Digital Licensing ().

 
Page updated 2019-12-25
Handle: RePEc:jof:jforec:v:26:y:2007:i:2:p:129-153