Forecasting Performance of Structural Time Series Models
用111个商业和经济时间序列数据，比较结构时间序列模型与四种复杂度相近方法的预测表现，发现结构模型在年度、季度和月度数据上表现良好，尤其对长期预测和季节性数据更准确。
Although theoretical research on the properties of structural time series models has regularly appeared in the literature, there is as yet scant evidence on the forecasting performance of structural models relative to more traditional methods. This study compares the empirical performance of structural time series models to four methods that are similar in complexity, using 111 business and economic time series. The structural approach appears to perform quite well on annual, quarterly, and monthly data, especially for long forecasting horizons and seasonal data. Of the more complex forecasting methods, structural models appear to be among the most accurate.