Sufficient forecasting using factor models

Jianqing Fan Lingzhou Xue Jiawei Yao

Statistics Theory and Methods mathscidoc:1912.43357

Journal of econometrics, 201, (2), 292-306, 2017.12
We consider forecasting a single time series when there is a large number of predictors and a possible nonlinear effect. The dimensionality was first reduced via a high-dimensional factor model implemented by the principal component analysis. Using the extracted factors, we develop a novel forecasting method called the sufficient forecasting, which provides a set of sufficient predictive indices, inferred from high-dimensional predictors, to deliver additional predictive power. The projected principal component analysis will be employed to enhance the accuracy of inferred factors when a semi-parametric factor model is assumed. Our method is also applicable to cross-sectional sufficient regression using extracted factors. The connection between the sufficient forecasting and the deep learning architecture is explicitly stated. The sufficient forecasting correctly estimates projection indices of the underlying factors even
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  title={Sufficient forecasting using factor models},
  author={Jianqing Fan, Lingzhou Xue, and Jiawei Yao},
  booktitle={Journal of econometrics},
Jianqing Fan, Lingzhou Xue, and Jiawei Yao. Sufficient forecasting using factor models. 2017. Vol. 201. In Journal of econometrics. pp.292-306.
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