common.predict: Forecasting the factor-driven common component

In fnets: Factor-Adjusted Network Estimation and Forecasting for High-Dimensional Time Series

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"))

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)

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

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

## Not run: 
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)

## End(Not run)

fnets documentation built on May 29, 2024, 8:42 a.m.