Forecasting using principal components from a large number of predictors

• 成果类型: Article
• 署名作者: Stock, JH; Watson, MW
• 署名单位: Harvard University; National Bureau of Economic Research; Princeton University; Princeton University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214502388618960
• 发表日期: 2002
• 页码: 1167-1179
• 关键词: arbitrage; variance

摘要

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.