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来源类型Working Paper
规范类型报告
DOI10.3386/w14284
来源IDWorking Paper 14284
Bayesian Averaging, Prediction and Nonnested Model Selection
Han Hong; Bruce Preston
发表日期2008-08-29
出版年2008
语种英语
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
URLhttps://www.nber.org/papers/w14284
来源智库National Bureau of Economic Research (United States)
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资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/571959
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GB/T 7714
Han Hong,Bruce Preston. Bayesian Averaging, Prediction and Nonnested Model Selection. 2008.
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