Journal article
Increase in mean square forecast error when omitting a needed covariate
International journal of forecasting, Vol.23(1), pp.147-152
01/2007
DOI: 10.1016/j.ijforecast.2006.10.001
Abstract
Mean square errors of ex-post and ex-ante forecasts from transfer function (regression) models are compared with mean square forecast errors of univariate time series models that ignore the covariate. We show that forecasts from the univariate ARMA models are never better, and are usually worse, than the forecasts from the transfer function model.
Details
- Title: Subtitle
- Increase in mean square forecast error when omitting a needed covariate
- Creators
- Johannes Ledolter - University of Iowa
- Resource Type
- Journal article
- Publication Details
- International journal of forecasting, Vol.23(1), pp.147-152
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.ijforecast.2006.10.001
- ISSN
- 0169-2070
- eISSN
- 1872-8200
- Language
- English
- Date published
- 01/2007
- Academic Unit
- Statistics and Actuarial Science; Business Analytics
- Record Identifier
- 9984380428302771
Metrics
4 Record Views