Fildes, Robert and Madden, Gary and Tan, Joachim (2007): Optimal forecasting model selection and data characteristics. Published in: Applied Financial Economics No. 17 (2007): pp. 1251-1264.
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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|
|Original Title:||Optimal forecasting model selection and data characteristics|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods; Simulation Methods
E - Macroeconomics and Monetary Economics > E1 - General Aggregative Models > E17 - Forecasting and Simulation: Models and Applications
|Depositing User:||Gary G Madden|
|Date Deposited:||29. Sep 2008 09:17|
|Last Modified:||16. Feb 2013 03:21|
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