Extract Factor Estimates in a Data Frame
# S3 method for dfm
as.data.frame(
x,
...,
method = "all",
pivot = c("long", "wide.factor", "wide.method", "wide", "t.wide"),
time = seq_row(x$F_pca),
stringsAsFactors = TRUE
)an object class 'dfm'.
not used.
character. The factor estimates to use: any of "qml", "2s", "pca" (multiple can be supplied) or "all" for all estimates.
character. The orientation of the frame: "long", "wide.factor" or "wide.method", "wide" or "t.wide".
a vector identifying the time dimension, or NULL to omit a time variable.
make factors from method and factor identifiers. Same as option to as.data.frame.table.
A data frame of factor estimates.
# \donttest{
library(xts)
# Fit DFM with 3 factors and 3 lags in the transition equation
mod = DFM(diff(BM14_M), r = 3, p = 3)
#> Converged after 26 iterations.
# Taking a single estimate:
print(head(as.data.frame(mod, method = "qml")))
#> Method Factor Time Value
#> 1 QML f1 1 3.0487579
#> 2 QML f1 2 -0.4236117
#> 3 QML f1 3 -9.4779810
#> 4 QML f1 4 -10.8844746
#> 5 QML f1 5 -6.0722144
#> 6 QML f1 6 -1.0811737
print(head(as.data.frame(mod, method = "qml", pivot = "wide")))
#> Time f1 f2 f3
#> 1 1 3.0487579 -4.371719 -0.1134302
#> 2 2 -0.4236117 -2.190223 -6.3464355
#> 3 3 -9.4779810 3.824410 -2.1678243
#> 4 4 -10.8844746 6.942244 0.4569135
#> 5 5 -6.0722144 1.303051 0.7505150
#> 6 6 -1.0811737 -2.430000 1.5735378
# Adding a proper time variable
time = index(BM14_M)[-1L]
print(head(as.data.frame(mod, method = "qml", time = time)))
#> Method Factor Time Value
#> 1 QML f1 1980-02-29 3.0487579
#> 2 QML f1 1980-03-31 -0.4236117
#> 3 QML f1 1980-04-30 -9.4779810
#> 4 QML f1 1980-05-31 -10.8844746
#> 5 QML f1 1980-06-30 -6.0722144
#> 6 QML f1 1980-07-31 -1.0811737
# All estimates: different pivoting methods
for (pv in c("long", "wide.factor", "wide.method", "wide", "t.wide")) {
cat("\npivot = ", pv, "\n")
print(head(as.data.frame(mod, pivot = pv, time = time), 3))
}
#>
#> pivot = long
#> Method Factor Time Value
#> 1 PCA f1 1980-02-29 0.8445713
#> 2 PCA f1 1980-03-31 0.5259228
#> 3 PCA f1 1980-04-30 -1.2107116
#>
#> pivot = wide.factor
#> Method Time f1 f2 f3
#> 1 PCA 1980-02-29 0.8445713 -0.7908231 -1.0289352
#> 2 PCA 1980-03-31 0.5259228 -0.6706157 -3.2251023
#> 3 PCA 1980-04-30 -1.2107116 0.0519631 0.9270935
#>
#> pivot = wide.method
#> Factor Time PCA TwoStep QML
#> 1 f1 1980-02-29 0.8445713 0.2903274 3.0487579
#> 2 f1 1980-03-31 0.5259228 -1.1656341 -0.4236117
#> 3 f1 1980-04-30 -1.2107116 -4.9014535 -9.4779810
#>
#> pivot = wide
#> Time f1_PCA f2_PCA f3_PCA f1_TwoStep f2_TwoStep f3_TwoStep
#> 1 1980-02-29 0.8445713 -0.7908231 -1.0289352 0.2903274 -1.26492938 -1.6769534
#> 2 1980-03-31 0.5259228 -0.6706157 -3.2251023 -1.1656341 -0.70548999 -5.5720725
#> 3 1980-04-30 -1.2107116 0.0519631 0.9270935 -4.9014535 0.06938226 -0.3158611
#> f1_QML f2_QML f3_QML
#> 1 3.0487579 -4.371719 -0.1134302
#> 2 -0.4236117 -2.190223 -6.3464355
#> 3 -9.4779810 3.824410 -2.1678243
#>
#> pivot = t.wide
#> Time f1_PCA f1_TwoStep f1_QML f2_PCA f2_TwoStep f2_QML
#> 1 1980-02-29 0.8445713 0.2903274 3.0487579 -0.7908231 -1.26492938 -4.371719
#> 2 1980-03-31 0.5259228 -1.1656341 -0.4236117 -0.6706157 -0.70548999 -2.190223
#> 3 1980-04-30 -1.2107116 -4.9014535 -9.4779810 0.0519631 0.06938226 3.824410
#> f3_PCA f3_TwoStep f3_QML
#> 1 -1.0289352 -1.6769534 -0.1134302
#> 2 -3.2251023 -5.5720725 -6.3464355
#> 3 0.9270935 -0.3158611 -2.1678243
# }