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学习、预测与结构突变AI 解读

Abstract We provide a general methodology for forecasting in the presence of structural breaks induced by unpredictable changes to model parameters. Bayesian methods of learning and model comparison are used to derive a predictive density that takes into account the possibility that a break will occur before the next observation. Estimates for the posterior distribution of the most recent break are generated as a by‐product of our procedure. We discuss the importance of…

随机趋势、确定性趋势与经济周期转折点AI 解读

This study examines the relationship between specifications for long-run output patterns and specifications for business cycle dynamics. In an application to US GDP, it is found that inferences about the nature of the trend in output are not robust to changes in the specification for short-run fluctuations. Similarly, the choice of which model best describes the transitory movements in output depends on the way in which the trend is specified. The empirical analysis makes…

随机趋势、确定性趋势与经济周期转折点AI 解读

This study examines the relationship between specifications for long-run output patterns and specifications for business cycle dynamics. In an application to US GDP, it is found that inferences about the nature of the trend in output are not robust to changes in the specification for short-run fluctuations. Similarly, the choice of which model best describes the transitory movements in output depends on the way in which the trend is specified. The empirical analysis makes…

随机贴现因子的贝叶斯估计AI 解读

This article provides a Bayesian method of estimating the marginal posterior distributions for stochastic discount factors associated with observed asset returns. These estimates can be used to provide measures of fit for asset pricing models and to identify broad features of the characteristics that should be explained. These measures of fit can be used to supplement model evaluation exercises based on L. P. Hansen-R. Jagannathan bounds.