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利用多个宏观经济模型进行预测AI 解读

We establish methods that improve the predictions of macroeconometric models—dynamic factor models, dynamic stochastic general equilibrium models, and vector autoregressions—using a quarterly U.S. data set. We measure prediction quality with one-step-ahead probability densities assigned in real time. Two steps lead to substantial improvements: (a) the use of full Bayesian predictive distributions rather than conditioning on the posterior mode for parameters and (b) the use…

多重结构断点模型中的推断与预测AI 解读

This paper develops a new Bayesian approach to structural break modeling. The focuses of the approach are the modeling of in-sample structural breaks and forecasting time series allowing out-of-sample breaks. The model has several desirable features. First, the number of regimes is not fixed but is treated as a random variable. Second, the model adopts a hierarchical prior for regime coefficients, which allows for the coefficients of one regime to contain information about…

宏观经济时间序列的先验及其应用AI 解读

This paper takes up Bayesian inference in a general trend stationary model for macroeconomic time series with independent Student- t disturbances. The model is linear in the data, but nonlinear in parameters. An informative but nonconjugate family of prior distributions for the parameters is introduced, indexed by a single parameter that can be readily elicited. The main technical contribution is the construction of posterior moments, densities, and odd ratios by using a…

十九个 OECD 国家实际 GDP 的长期与周期行为：1957—1983年AI 解读

Log per capita real gross domestic product is modeled as a third-order autoregression with a pair of complex roots whose amplitude is smaller than the amplitude of the real root. The behavior of this terms series is interpreted in terms of these two amplitudes, the periodicity of the complex roots, and the standard deviation of the disturbance. Restrictions are evaluated and inference is conducted using the likelihood principle, applying Monte Carlo integration with…

宏观时间序列单变量自回归预测程序的比较AI 解读

The actual performance of several automated univariate autoregressive forecasting procedures, applied to 150 macroeconomic time series, are compared. The procedures are the random walk model as a basis for comparison; long autoregressions, with three alternative rules for lag length selection; and a long autoregression estimated by minimizing the sum of absolute deviations. The sensitivity of each procedure to preliminary transformations, data, periodicity, forecast horizon,…

多个时间序列之间线性依赖与反馈的度量AI 解读

Measures of linear dependence and feedback for multiple time series are defined. The measure of linear dependence is the sum of the measure of linear feedback from the first series to the second, linear feedback from the second to the first, and instantaneous linear feedback. The measures are nonnegative, and zero only when feedback (causality) of the relevant type is absent. The measures of linear feedback from one series to another can be additively decomposed by…

两个协方差平稳时间序列独立性检验的比较AI 解读

The approximate slopes of several tests of the independence of two covariance stationary time series are derived and compared. It is shown that the approximate slopes of regression tests are at least as great as those based on the residuals of univariate ARIMA models, and that there are cases in which the former are arbitrarily great while the latter are arbitrarily small. These analytical findings are supported by a Monte Carlo study that shows that in samples of size 100…