Optimal forecasting model selection and data characteristics

Fildes, Robert; Madden, Gary and Tan, Joachim (2007): Optimal forecasting model selection and data characteristics. Published in: Applied Financial Economics No. 17 (2007): pp. 1251-1264.

Abstract

Selection 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.

• Item Type: MPRA Paper
• Language: English
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Other Model Applications; E - Macroeconomics and Monetary Economics > E1 - General Aggregative Models > E17 - Forecasting and Simulation

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