SF.CI: Conformal inference of the sufficient forecasting

In sufficientForecasting: Sufficient Forecasting using Factor Models

Conformal inference of the sufficient forecasting

Description

Conformal inference of the sufficient forecasting

Usage

SF.CI(
  y,
  X,
  newX = NULL,
  type = "LM",
  K = "default",
  L = 1,
  alpha = 0.1,
  discretization = TRUE,
  nslices = 10
)

Arguments

y	Response, T by 1 matrix
X	Predictors, p by T matrix
newX	New predictors, a vector contains p entries (or NULL)
type	LM or LLM (default = LM)
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)
alpha	Mis-coverage rate
discretization	Hyperparameter in SIR (default = TRUE)
nslices	Hyperparameter in SIR (default = 10)

Value

A list with components

yhat

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

ci_lower

Lower bound of conformal interval

ci_upper

Upper bound of conformal interval

References

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.CI(dataExample$y,dataExample$X,type = "LM",alpha = 0.05)

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