SF: Select a method from PC, SIR and DR to do point prediction

In sufficientForecasting: Sufficient Forecasting using Factor Models

Select a method from PC, SIR and DR to do point prediction

Description

Select a method from PC, SIR and DR to do point prediction

Usage

SF(
  y,
  X,
  newX = NULL,
  K = "default",
  L = 1,
  method = "SIR.LM",
  hyperparameter = list()
)

Arguments

y	Response, T by 1 matrix
X	Predictors, p by T matrix
newX	New predictors, a vector contains p entries (or NULL)
K	The number of common factors (default = obtained by getK)
L	The number of predictive indices, L is required to be no greater than K (default = 1)
method	Select one from PC, SIR.LM, SIR.LLM and DR to do point prediction (default = SIR.LM)
hyperparameter	A list of parameters for the corresponding method For SIR.LM and SIR.LLM: arguments discretization and nslices. See SF.SIR for detail For DR: arguments etaopg and nslices. See SF.DR for detail

Value

Out-of-sample forecast for newX; or in-sample forecast for the last observed data point if newX is NULL

References

Fan, J., Xue, L. and Yao, J. (2017), Sufficient forecasting using factor models, Journal of econometrics 201(2), 292–306

Luo, W., Xue, L., Yao, J. and Yu, X. (2022), Inverse moment methods for sufficient forecasting using high-dimensional predictors, Biometrika 109(2), 473–487.

Yu, X., Yao, J. and Xue, L. (2022), Nonparametric estimation and conformal inference of the sufficient forecasting with a diverging number of factors, Journal of Business & Economic Statistics 40(1), 342–354.

Examples

utils::data(dataExample,package = "sufficientForecasting")
SF(dataExample$y,dataExample$X,method = "SIR.LLM",
hyperparameter = list(nslices = 5,discretization = TRUE))
SF(dataExample$y,dataExample$X,dataExample$newX,method = "DR")
SF(dataExample$y,dataExample$X,dataExample$newX,method = "PC")

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