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预测不确定性、预测分歧与线性池AI 解读

Summary The linear pool is the most popular method for combining density forecasts. We analyze its implications concerning forecast uncertainty, using a new framework that focuses on the means and variances of the individual and combined forecasts. Our results show that, if the variance predictions of the individual forecasts are unbiased, the well‐known “disagreement” component of the linear pool exacerbates the upward bias of its variance prediction. This finding suggests…

我们能预测多远？预测信息含量的统计检验AI 解读

Summary We develop tests for the null hypothesis that forecasts become uninformative beyond some maximum forecast horizon h ∗ . The forecast may result from a survey of forecasters or from an estimated parametric model. The first class of tests compares the mean‐squared prediction error of the forecast to the variance of the evaluation sample, whereas the second class of tests compares it with the mean‐squared prediction error of the recursive mean. We show that the forecast…

利用原点矩评估多步密度预测的校准性AI 解读

The evaluation of multi-step-ahead density forecasts is complicated by the serial correlation of the corresponding probability integral transforms. In the literature, three testing approaches can be found that take this problem into account. However, these approaches rely on data-dependent critical values, ignore important information and, therefore lack power, or suffer from size distortions even asymptotically. This article proposes a new testing approach based on raw…

基于马尔可夫转换截距自回归的经济周期非对称性检验AI 解读

Implications of two concepts of asymmetry—deepness and steepness—are investigated for autoregressive processes with a Markov-switching intercept. The formulas for the skewness of these processes and the skewness of the first differences of these processes are derived. The parameter restrictions leading to nondeepness and nonsteepness are presented for the special case of a first-order autoregression and two states. It is shown that these restrictions imply that previous…