Can lead to a better understanding of the nature of any nonstationary process among the different component series.
Usage
vecm(
vintages.view,
lag = 2,
model = c("none", "cnt", "trend"),
na.zero = FALSE
)Arguments
- vintages.view
mts object. Vertical or diagonal view of the
create_vintages()output- lag
Number of lags
- model
Character. Must be "none" (the default), "cnt" or "trend".
- na.zero
Boolean whether missing values should be considered as 0 or rather as data not (yet) available (the default).
Examples
## Simulated data
df_long <- simulate_long(
n_period = 10L * 4L,
n_revision = 5L,
periodicity = 4L,
start_period = as.Date("2010-01-01")
)
## Create vintage and test
vintages <- create_vintages(df_long, periodicity = 4L)
vecm(vintages$diagonal_view)
#> trace(2) trace(1) max(2) max(1)
#> [Release[1]]_[Release[2]] 3.148075268 12.259579 3.148075268 9.111504
#> [Release[1]]_[Release[3]] 2.029394378 9.167987 2.029394378 7.138593
#> [Release[1]]_[Release[4]] 1.974699818 12.379551 1.974699818 10.404851
#> [Release[1]]_[Release[5]] 0.121579484 7.913927 0.121579484 7.792347
#> [Release[2]]_[Release[3]] 2.716891232 15.836457 2.716891232 13.119566
#> [Release[2]]_[Release[4]] 1.953905182 6.413634 1.953905182 4.459729
#> [Release[2]]_[Release[5]] 0.341334751 9.378903 0.341334751 9.037568
#> [Release[3]]_[Release[4]] 1.817419613 11.724168 1.817419613 9.906748
#> [Release[3]]_[Release[5]] 0.104087544 5.751848 0.104087544 5.647760
#> [Release[4]]_[Release[5]] 0.004372904 12.642948 0.004372904 12.638576