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结构变化时点的条件概率评估AI 解读

Abstract We propose a strategy for assessing structural stability in time‐series frameworks when potential change dates are unknown. Existing stability tests are effective in detecting structural change, but procedures for identifying timing are imprecise, especially in assessing the stability of variance parameters. We present a likelihood‐based procedure for assigning conditional probabilities to the occurrence of structural breaks at alternative dates. The procedure is…

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凯恩斯式冲击与索洛残差：识别经济周期波动来源AI 解读

We employ a neoclassical business-cycle model to study two sources of business-cycle fluctuations: marginal efficiency of investment shocks, and total factor productivity shocks. The parameters of the model are estimated using a Bayesian procedure that accommodates prior uncertainty about their magnitudes; from these estimates, posterior distributions of the two shocks are obtained. The postwar US experience suggests that both shocks are important in understanding…

无需预先确定平稳化方式的股票价格建模：计算更正AI 解读

In “Modeling Stock Prices without Knowing How to Induce Stationarity” (1994, Econometric Theory 10, 701–719), we used posterior-odds calculations to evaluate restrictions imposed by a present-value model of stock prices across the equations of a VAR representation of stock prices and dividends. The results we reported are tainted by the omission of two factors: the Jacobians induced by the mapping of our priors over VAR parameters β into the restricted sample spaces relevant…

校准的贝叶斯方法AI 解读

The authors develop a Bayesian approach to calibration which enables the incorporation of uncertainty regarding the parameters of the theoretical model under investigation. Their procedure involves the specification of prior distributions over parameter values, which in turn induce distributions over the statistical properties of artificial data simulated from the model. These distributions are compared with their empirical counterparts in order to assess the model's fit.…

在未知平稳化方式下建模股票价格AI 解读

Bayesian procedures for evaluating linear restrictions imposed by economic theory on dynamic econometric models are applied to a simple class of presentvalue models of stock prices. The procedures generate inferences that are not conditional on ancillary assumptions regarding the nature of the nonstationarity that characterizes the data. Inferences are influenced by prior views concerning nonstationarity, but these views are formally incorporated into the analysis, and…

移动平均参数估计：经典估计的堆积现象与贝叶斯后验AI 解读

The authors analyze posterior distributions of the moving average parameter in the first-order case and sampling distributions of the corresponding maximum likelihood estimator. Sampling distributions 'pile up' at unity when the true parameter is near unity; hence, if one were to difference such a process, estimates of the moving average component of the resulting series would spuriously tend to indicate that the process was overdifferenced. Flat-prior posterior…

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支持趋势平稳的证据比我们原先认为的更强AI 解读

Abstract In DeJong and Whiteman (1991a), we concluded that 11 of the 14 macroeconomic time‐series originally studied by Nelson and Plosser (1982) supported trend‐stationarity. Phillips (1991) criticizes this inference, claiming that our procedure is biased against integration, and that our results are sensitive to model and prior specification. However, Phillips' alternative models and priors bias his results in favour of integration; despite these biases, Phillips' own…