Bayesian model selection and prediction with empirical applications
基于PIC准则选择自回归模型的滞后阶数、趋势阶数和单位根，并开发了贝叶斯模型的预测包含检验，应用于Nelson-Plosser和Schotman-van Dijk数据，发现简约的演化格式模型优于固定格式模型。
This paper builds on some recent work by the author and Werner Ploberger (1991, 1994) on the development of ‘Bayes models’ for time series and on the authors' model selection criterion ‘PIC’. The PIC criterion is used in this paper to determine the lag order, the trend degree and the presence or absence of a unit root in an autoregression with deterministic trend. A new forecast-encompassing test for Bayes models is developed which allows one Bayes model to be compared with another on the basis of their respective forecasting performance. The paper reports an extended empirical application of the methodology to the Nelson-Plosser (1982) and Schotman-van Dijk (1991) data. It is shown that parsimonious evolving-format Bayes models forecastencompass fixed Bayes models of the ‘AR(3) + linear trend’ variety for most of these series. In some cases, the forecast performance of the parsimonious Bayes models is substantially superior. The results cast some doubts on the value of working with fixedformat time series models in empirical research and demonstrate the practical advantages of evolving-format models. The paper makes a new suggestion for modelling interest rates in terms of reciprocals of levels rather than levels (which display more volatility) and shows that the best data-determined model for this transformed series is a martingale.