Forecasting with quantitative methods: the impact of special events in time series

Konstantinos Nikolopoulos ()

Applied Economics, 2010, vol. 42, issue 8, 947-955

Abstract: Quantitative methods are very successful in producing baseline forecasts of time series; however, these models forecast neither the timing nor the impact of special events such as promotions or strikes. In most of the cases, the timing of such events is not known so they are usually referred as shocks (economics) or special events (forecasting). Sometimes the timing of such events is known a priori (i.e. a future promotion); but even then the impact of the forthcoming event is hard to estimate. Forecasters prefer to use their own judgement for adjusting for forthcoming special events, but human efficiency in such tasks has been found to be deficient. This study after examining the relative performance of Artificial Neural Networks (ANNs), Multiple Linear Regression (MLR) and Nearest Neighbour (NN) approaches proposes an expert method, which combines the strengths of regression and artificial intelligence.

Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/00036840701721042 (text/html)
Access to full text is restricted to subscribers.

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:taf:applec:v:42:y:2010:i:8:p:947-955

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEC20

DOI: 10.1080/00036840701721042

Access Statistics for this article

Applied Economics is currently edited by Anita Phillips

More articles in Applied Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().