Codes accompanying "Tail-robust factor modelling of vector and tensor time series in high dimensions" by Barigozzi, Cho and Maeng (2024).
Contains the main routine rob.tfa for the proposed robust factor analysis.
Contains the codes for generating the data used in simulation studies.
Contains the FREMD-MD data analysed in Section 6.1; accessed from https://research.stlouisfed.org/econ/mccracken/fred-databases/ on 01-03-2024. We provide the csv file to ensure consistency with the data analysis reported in the paper.
Contains codes for reproducing the rolling window-based forecasting results in Section 6.1
Contains the EA-MD data analysed in Section 6.2.
Contains codes for reproducing the results in Section 6.2
source('codes.R')
source('dgp.R')
td <- tensor_dgp(n, case = 2, dist = 't', perc = .05)
rtfa <- rob.tfa(td$x, r = c(3, 3, 3))
plot(as.numeric(dimnames(rtfa$tau.cv)[[1]]), apply(rtfa$tau.cv[,2,,], 1, mean), type = 'b',
log = "x", xlab = expression(tau), ylab = "CV")
abline(v = rtfa$tau, col = 2)