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]] 0.987357 9.784201 0.987357 8.796844
#> [Release[1]]_[Release[3]] 3.741767 16.296444 3.741767 12.554677
#> [Release[1]]_[Release[4]] 10.991390 31.146069 10.991390 20.154680
#> [Release[1]]_[Release[5]] 5.532100 203.671471 5.532100 198.139371
#> [Release[2]]_[Release[3]] 3.872700 16.537360 3.872700 12.664660
#> [Release[2]]_[Release[4]] 7.597858 19.233868 7.597858 11.636010
#> [Release[2]]_[Release[5]] 3.945131 NaN 3.945131 NaN
#> [Release[3]]_[Release[4]] 2.367197 15.350389 2.367197 12.983192
#> [Release[3]]_[Release[5]] 3.983886 NaN 3.983886 NaN
#> [Release[4]]_[Release[5]] 1.254674 NaN 1.254674 NaN