allisterh / dffm

R-package accompanying the paper "Approximate Factor Models for Functional Time Series"

dffm

The goal of dffm is to provide functionality to apply the methods developed in the paper “Approximate Factor Models for Functional Time Series” by Sven Otto and Nazarii Salish.

Preprint:

https://arxiv.org/abs/2201.02532

Installation

You can install the package using the following command:

library(remotes)
install_github("ottosven/dffm")

Example

library(dffm)
# ---- data ---- #
data = load.fed()
# ---- preliminary --- #
fpca.obj = fpca.preprocess(data = data, method = "splines")
dffm.3Dplot(fpca.obj, domainlab = "maturities", outputlab = "yields (in percent)", 
            main = "3D plot of FED yields from 2001 to 2021")
plot(fpca.obj$eigenvalues, type = "b", main = "Screeplot", ylab = "eigenvalues")
dffm.criterion(fpcaobj = fpca.obj)
# ---- fitting yieldcurve ---- #
dffm.obj = dffm(fpcaobj = fpca.obj, K = 4, p = 1)
plot(dffm.obj$fittedcurve.workgrid[246,], type = "l", xlab = "maturities", ylab = "yields (in percent)", 
     main = "Yieldcurve of december 2021")
# ---- predicting the yieldcurve ---- #
predicted.dffm = dffm.forecast(dffmobj = dffm.obj, h = 10)
plot(predicted.dffm$predcurves.workgrid[10,], type = "l", xlab = "maturities", ylab = "yields (in percent)", main = "Predicted yieldcurve of october 2022")