Forecasting the Product of Two Time Series with a Linear Asymmetric Error Cost Function
AbstractOur objective is to present a methodology which minimizes the expected cost of predictive errors when: (a) predictions are obtained for the product of two separately attained but contemporaneous time series, and (b) a linear asymmetric error cost function reflects the costs associated with predictive errors. Integrated Autoregressive-Moving Average models characterize the two series. Each prediction is expressed as a quantile of the conditional distribution of the contemporaneous product of the two series (i.e., a quantile of the distribution of the product of two Gaussian random variables with nonzero means). An actually observed hospital food service demand problem exemplifies the procedure and utility of our methodology.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by INFORMS in its journal Management Science.
Volume (Year): 24 (1978)
Issue (Month): 6 (February)
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Goodwin, P., 1996. "Statistical correction of judgmental point forecasts and decisions," Omega, Elsevier, vol. 24(5), pages 551-559, October.
- Goodwin, Paul, 2005. "Providing support for decisions based on time series information under conditions of asymmetric loss," European Journal of Operational Research, Elsevier, vol. 163(2), pages 388-402, June.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mirko Janc).
If references are entirely missing, you can add them using this form.