An Alternative Approach to Modeling and Forecasting Seasonal Time Series
提出一种基于贝叶斯自回归的季节性建模方法，将季节性直接纳入系数先验，并用10个美国季度宏观序列检验其预测表现，与五种常用模型对比。
This article proposes an alternative methodology for modeling and forecasting seasonal series. The approach is in the Bayesian autoregression tradition pioneered by Doan, Litterman, and Sims and builds seasonality directly into the prior of the coefficients of the model by means of a set of uncertain linear restrictions. As an illustration, the method is applied to 10 U.S. quarterly macroeconomic series. For each series, I compare the forecasting performance of a univariate time-varying autoregressive model with seasonality built in the prior of the coefficients with five other widely used models.