sufficientForecasting (version 0.1.0)

SF.SIR: Sliced inverse regression for sufficient forecasting

Description

Sliced inverse regression for sufficient forecasting

Usage

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

Value

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

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), type = LM fits a linear regression of the response on the estimated predictive indices; type = LLM fits a local linear regression

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)

discretization

Hyperparameter in SIR (default = TRUE)

nslices

Hyperparameter in SIR (default = 10)

References

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

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.SIR(dataExample$y,dataExample$X,type = "LLM")