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  • 标题:Optimal Prediction Pools
  • 本地全文:下载
  • 作者:John Geweke ; Gianni Amisano
  • 期刊名称:Euro Area Balance of Payments and International Investment Position Statistics
  • 印刷版ISSN:1830-3420
  • 电子版ISSN:1830-3439
  • 出版年度:2009
  • 卷号:1
  • 出版社:European Central Bank
  • 摘要:A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or linear pools, evaluated using the conventional log predictive scoring rule. The log score is a concave function of the weights and, in general, an optimal linear combination will include several models with positive weights despite the fact that exactly one model has limiting posterior probability one. The paper derives several interesting formal results: for example, a prediction model with positive weight in a pool may have zero weight if some other models are deleted from that pool. The results are illustrated using S&P 500 returns with prediction models from the ARCH, stochastic volatility and Markov mixture families. In this example models that are clearly inferior by the usual scoring criteria have positive weights in optimal linear pools, and these pools substantially outperform their best components.
  • 关键词:forecasting; GARCH; log scoring; Markov mixture; model combination;S&P 500 returns; stochastic volatility
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