sufficientForecasting-package: sufficientForecasting: Sufficient Forecasting using Factor...

In sufficientForecasting: Sufficient Forecasting using Factor Models

sufficientForecasting: Sufficient Forecasting using Factor Models

Description

The sufficient forecasting (SF) method is implemented by this package for a single time series forecasting using many predictors and a possibly nonlinear forecasting function. Assuming that the predictors are driven by some latent factors, the SF first conducts factor analysis and then performs sufficient dimension reduction on the estimated factors to derive predictive indices for forecasting. The package implements several dimension reduction approaches, including principal components (PC), sliced inverse regression (SIR), and directional regression (DR). Methods for dimension reduction are as described in: Fan, J., Xue, L. and Yao, J. (2017) doi: 10.1016/j.jeconom.2017.08.009, Luo, W., Xue, L., Yao, J. and Yu, X. (2022) doi: 10.1093/biomet/asab037 and Yu, X., Yao, J. and Xue, L. (2022) doi: 10.1080/07350015.2020.1813589.

Author(s)

Maintainer: Jing Fu [e-mail]

Authors:

• Jianqing Fan

• Wei Luo

• Lingzhou Xue

• Jiawei Yao

• Xiufan Yu

See Also

Useful links:

• https://github.com/JingFu1224/sufficientForecasting

• Report bugs at https://github.com/JingFu1224/sufficientForecasting/issues

sufficientForecasting documentation built on March 7, 2023, 6:13 p.m.