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sufficientForecasting  

Sufficient Forecasting using Factor Models
View on CRAN: Click here


Download and install sufficientForecasting package within the R console
Install from CRAN:
install.packages("sufficientForecasting")

Install from Github:
library("remotes")
install_github("cran/sufficientForecasting")

Install by package version:
library("remotes")
install_version("sufficientForecasting", "0.1.0")



Attach the package and use:
library("sufficientForecasting")
Maintained by
Jing Fu
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-02-17
Latest Update: 2023-02-17
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>.
How to cite:
Jing Fu (2023). sufficientForecasting: Sufficient Forecasting using Factor Models. R package version 0.1.0, https://cran.r-project.org/web/packages/sufficientForecasting. Accessed 31 Jan. 2025.
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Complete documentation for sufficientForecasting
Functions, R codes and Examples using the sufficientForecasting R package
Some associated functions: SF.CI . SF.DR . SF.PC . SF.SIR . SF . dataExample . getK . sufficientForecasting-package . 
Some associated R codes: SF.CI.R . SF.DR.R . SF.PC.R . SF.R . SF.SIR.R . css.R . dataExample.R . getK.R . sir.R . sufficientForecasting-package.R .  Full sufficientForecasting package functions and examples
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