Time Series Concepts for Conditional Distributions*
探讨当时间序列分析从关注条件均值和方差转向无条件分布时，原有概念（如断点、季节性、趋势、制度转换）是否仍然适用，并讨论预测、持续性、因果性等概念的推广。
Abstract The paper asks the question – as time series analysis moves from consideration of conditional mean values and variances to unconditional distributions, do some of the familiar concepts devised for the first two moments continue to be helpful in the more general area? Most seem to generalize fairly easy, such as the concepts of breaks, seasonality, trends and regime switching. Forecasting is more difficult, as forecasts become distributions, as do forecast errors. Persistence can be defined and also common factors by using the idea of a copula. Aggregation is more difficult but causality and controllability can be defined. The study of the time series of quantiles becomes more relevant.