tsBlocks: Construct predictor blocks for time series models

In robustHD: Robust Methods for High-Dimensional Data

Construct predictor blocks for time series models

Description

Construct blocks of original and lagged values for autoregressive time series models with exogenous inputs. The typical use case is to supply the output as newdata argument to the predict method of robust groupwise least angle regression models.

Usage

tsBlocks(x, y, p = 2, subset = NULL, intercept = TRUE)

Arguments

x	a numeric matrix or data frame containing the exogenous predictor series.
y	a numeric vector containing the response series.
p	an integer giving the number of lags to include (defaults to 2).
subset	a logical or integer vector defining a subset of observations from which to construct the matrix of predictor blocks.
intercept	a logical indicating whether a column of ones should be added to the matrix of predictor blocks to account for the intercept.

Value

A matrix containing blocks of original and lagged values of the time series y and x.

Author(s)

Andreas Alfons

See Also

predict.tslars, tslars, predict.tslarsP, tslarsP

robustHD documentation built on Sept. 27, 2023, 1:07 a.m.