tslars {tslars}R Documentation

Function to obtain the selected model accoring the the time series LARS algorithm

Description

The tslars function applies a dynamic variable selection procedure. It is an extension of the LARS algorithm of Efron et al (2004) which is designed for time series analysis. It provides a ranking of the predictors and a selection of which predictors to include in the final model as well as a selection of the appropriate lag length.

Usage

tslars(formula, h = 1, p.max = 5, max.x = 10, nr.rank = NA)

Arguments

formula a formula describing the model to be fitted
h the forecast horizon, defaults to 1.
p.max the maximal number of lags to allow, defaults to 5.
max.x the maximal number of predictors to include in the final model, defaults to 10.
nr.rank the number of predictors to be ranked. This is especially interesting if the total number of predictors is really large.

Value

A tslars-object is returned, for which print(), summary(), predict() and coef() are available. An object of class "lm" is a list containing the following components:

active the active set, a vector giving the TS-LARS ordering of the predictors, '0' indicates lagged values of the response.
fixedp indicates whether the lag length was prespecified (TRUE) or not (FALSE).
laglength.opt if fixedp is TRUE, the prespecified lag length. If fixedp is FALSE, the optimal lag length selected according to BIC.
nrvar.opt the optimal number of predictors to include in the final model, according to the BIC.
bic the BIC values for the nested models.
h the forecast horizon used.
call the matched call.
response the response used.
predictors the predictors used.

Author(s)

Sarah Gelper

References

Gelper, S. and Croux, C. (2009) Time series least angle regression for selecting predictive economic sentiment series. www.econ.kuleuven.be/sarah.gelper/public

Examples


n <- 100
m <- 10 #m>5
x <- matrix(rnorm(n*m), ncol=m)
coefs <- c(rep(1,5),rep(0,m-5))
y <- c(rnorm(1),crossprod(t(x[1:(n-1),]),coefs) + rnorm(n-1))

mytslars <- tslars(y~x)
summary(mytslars)
# To obtain an h-step-ahead prediction of the response using the selected model fitted by OLS:
myprediction <- predict(mytslars) 


[Package tslars version 1.0 Index]