Optimal forecasting model selection and data characteristics
AbstractSelection protocols such as Box–Jenkins, variance analysis, method switching and rules-based forecasting measure data characteristics and incorporate them in models to generate best forecasts. These protocol selection methods are judgemental in application and often select a single (aggregate) model to forecast a collection of series. An alternative is to apply individually selected models for to series. A multinomial logit (MNL) approach is developed and tested on Information and communication technology share price data. The results suggest the MNL model has the potential to predict the best forecast method based on measurable data characteristics.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 10819.
Date of creation: 2007
Date of revision:
Other versions of this item:
- Robert Fildes & Gary Madden & Joachim Tan, 2007. "Optimal forecasting model selection and data characteristics," Applied Financial Economics, Taylor & Francis Journals, vol. 17(15), pages 1251-1264.
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
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