common.predict function

Forecasting the factor-driven common component

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

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)

Returns

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

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)

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).