SF.PC: Principal component regression for sufficient forecasting

In sufficientForecasting: Sufficient Forecasting using Factor Models

Principal component regression for sufficient forecasting

Description

Principal component regression for sufficient forecasting

Usage

SF.PC(y, X, newX = NULL, K = "default", L = "default")

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 principal components used in the prediction, L is required to be no greater than K (default = K)

Value

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

Examples

utils::data(dataExample,package = "sufficientForecasting")
SF.PC(dataExample$y,dataExample$X)

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