Forecast combination and Bayesian model averaging: A prior sensitivity analysis

Arbeitspapier

In this study we evaluate the forecast performance of model averaged forecasts based on the predictive likelihood carrying out a prior sensitivity analysis regarding Zellner's g prior. The main results are fourfold: First the predictive likelihood does always better than the traditionally employed 'marginal' likelihood in settings where the true model is not part of the model space. Secondly, forecast accuracy as measured by the root mean square error (rmse) is maximized for the median probability model. On the other hand, model averaging excels in predicting direction of changes. Lastly, g should be set according to Laud and Ibrahim (1995) with a hold-out sample size of 25% to minimize the rmse (median model) and 75% to optimize direction of change forecasts (model averaging). We finally apply the aforementioned recommendations to forecast the monthly industrial production output of six countries beating for almost all countries the AR (1) benchmark model.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Papers in Economics and Finance ; No. 2010-14

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Statistical Simulation Methods: General ; Forecasting Models; Simulation Methods
• Subject: Forecast combination ; Bayesian model averaging ; median probability model ; predictive likelihood ; industrial production ; model uncertainty

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Feldkircher, Martin
• Event: Veröffentlichung
• (who): University of Salzburg, Department of Social Sciences and Economics
• (where): Salzburg
• (when): 2010

Further information

• Handle: http://hdl.handle.net/10419/71846
• Last update: 10.03.2025, 11:42 AM CET