Forecasting using principal components from a large number of predictors
Forecasting using principal components from a large number of predictors
复制标题
DOI：
10.1198/016214502388618960
复制
发表时间：
2002-12-01
影响因子：
3.7
通讯作者：
Watson, MW
中科院分区：
文献类型：
--
作者：
Stock, JH;Watson, MW
本文考虑在有多个预测因子(N)和时间序列观测值(T)的情况下预测单个时间序列。当数据遵循近似因子模型时，可以用少量指标来总结预测因子，我们使用主成分来估计这些指标；可行预测是渐近有效的，因为当N和T增大时，使用因子的实际值构造的可行预测和不可行预测之间的差概率收敛于0。即使在因子模型中存在时间变化，估计的因子也显示出一致性。
This article considers forecasting a single time series when there are many predictors (N) and time series observations (T). When the data follow an approximate factor model, the predictors can be summarized by a small number of indexes, which we estimate using principal components; Feasible forecasts are shown to be asymptotically efficient in the sense that the difference between the feasible forecasts and the infeasible forecasts constructed using the actual values of the factors converges in probability to 0 as both N and T grow large. The estimated, factors are shown to be consistent, even in the presence of time variation in the factor model.