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稳定季节模式模型的预测：夏威夷旅游数据应用AI 解读

We propose several variations of the stable seasonal pattern (SSP) model first introduced by Marshall and Oliver and study their prediction procedures. Depending on the type of data (count data or continuous variable), we propose different treatments. Previously SSP models have been applied to trendless data or adapted to trending data in ad hoc ways. In the models considered here, conditional independence allows the seasonal pattern and trend to be modeled separately,…

大冲击、小冲击与经济波动：宏观时间序列中的异常值AI 解读

Abstract We analyse fifteen post‐World War II US macroeconomic time series using a modified outlier identification procedure based on Tsay (1988a). ‘Large shocks’ appear to be present in all the series we examined. Furthermore, there are three basic outlier patterns: (1) outliers seem to be associated with business cycles, (2) outliers are clustered together—both over time and across series, (3) there appears to be a dichotomy between outlier behaviour of real versus nominal…

The authors propose some Stein-rule combination forecasting methods that are designed to ameliorate the estimation of risk inherent in making operational the variance-covariance method for constructing combination weights. By Monte Carlo simulation, it is shown that this amelioration can be substantial in many cases. Moreover, generalized Stein-rule combinations are proposed that offer the user the opportunity to enhance combination forecasting performance when shrinking the…

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约束最小二乘背景下主成分回归的一项最优性质AI 解读

A new optimal property for principal components regression is presented. In particular, it is shown that the trace of the covariance matrix for estimators obtained by deleting principal components associated with the smallest eigenvalues is at least as small as that for any other least-squares estimator with an equal or smaller number of linear restrictions. This property is useful in suggesting data transformations and determining the maximum variance reduction obtainable…