| 来源类型 | Working Paper
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| 规范类型 | 报告
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| DOI | 10.3386/w14284
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| 来源ID | Working Paper 14284
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| Bayesian Averaging, Prediction and Nonnested Model Selection |
| Han Hong; Bruce Preston
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| 发表日期 | 2008-08-29
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| 出版年 | 2008
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| 语种 | 英语
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| 摘要 | This paper studies the asymptotic relationship between Bayesian model averaging and post-selection frequentist predictors in both nested and nonnested models. We derive conditions under which their difference is of a smaller order of magnitude than the inverse of the square root of the sample size in large samples. This result depends crucially on the relation between posterior odds and frequentist model selection criteria. Weak conditions are given under which consistent model selection is feasible, regardless of whether models are nested or nonnested and regardless of whether models are correctly specified or not, in the sense that they select the best model with the least number of parameters with probability converging to 1. Under these conditions, Bayesian posterior odds and BICs are consistent for selecting among nested models, but are not consistent for selecting among nonnested models. |
| 主题 | Econometrics
; Estimation Methods
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| URL | https://www.nber.org/papers/w14284
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| 来源智库 | National Bureau of Economic Research (United States)
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| 引用统计 |
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| 资源类型 | 智库出版物
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| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/571959
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推荐引用方式
GB/T 7714 |
Han Hong,Bruce Preston. Bayesian Averaging, Prediction and Nonnested Model Selection. 2008.
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文件名：
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w14284.pdf
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格式：
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Adobe PDF
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