fnets (version 0.1.6)

common.predict: Forecasting the factor-driven common component

Description

Produces forecasts of the common component for a given forecasting horizon by estimating the best linear predictors

Usage

common.predict(object, x, n.ahead = 1, fc.restricted = TRUE, r = c("ic", "er"))

Value

a list containing

is

in-sample estimator of the common component (with each column representing a variable)

fc

forecasts of the common component for a given forecasting horizon h (with each column representing a variable)

r

restricted factor number

n.ahead

forecast horizon

Arguments

object

fnets object

x

input time series matrix, with each row representing a variable

n.ahead

forecasting horizon

fc.restricted

whether to forecast using a restricted or unrestricted, blockwise VAR representation of the common component

r

number of restricted factors, or a string specifying the factor number selection method when fc.restricted = TRUE; possible values are:

"ic"

information criteria of Alessi, Barigozzi & Capasso (2010))

"er"

eigenvalue ratio of Ahn & Horenstein (2013)

References

Ahn, S. C. & Horenstein, A. R. (2013) Eigenvalue ratio test for the number of factors. Econometrica, 81(3), 1203--1227.

Alessi, L., Barigozzi, M., and Capasso, M. (2010) Improved penalization for determining the number of factors in approximate factor models. Statistics & Probability Letters, 80(23-24):1806–1813.

Barigozzi, M., Cho, H. & Owens, D. (2024+) FNETS: Factor-adjusted network estimation and forecasting for high-dimensional time series. Journal of Business & Economic Statistics (to appear).

Forni, M., Hallin, M., Lippi, M. & Reichlin, L. (2005) The generalized dynamic factor model: one-sided estimation and forecasting. Journal of the American Statistical Association, 100(471), 830--840.

Forni, M., Hallin, M., Lippi, M. & Zaffaroni, P. (2017) Dynamic factor models with infinite-dimensional factor space: Asymptotic analysis. Journal of Econometrics, 199(1), 74--92.

Owens, D., Cho, H. & Barigozzi, M. (2024+) fnets: An R Package for Network Estimation and Forecasting via Factor-Adjusted VAR Modelling. The R Journal (to appear).

Examples

if (FALSE) {
out <- fnets(data.unrestricted, q = NULL, var.order = 1, var.method = "lasso",
do.lrpc = FALSE, var.args = list(n.cores = 2))
cpre <- common.predict(out)
ipre <- idio.predict(out, cpre)
}