Linear regression model of the revisions (R) on a preliminary vintage (P)
Arguments
- vintages.view
mts object. Vertical or diagonal view of the
create_vintages()output- gap
Integer. Gap to consider between each vintages. Default is 1 which means that revisions are calculated and tested for each vintages consecutively.
- 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)
efficiencyModel1(vintages[["diagonal_view"]])
#> N R2 F intercept.estimate
#> [Release[2]]-[Release[1]] 28 0.008492489 0.2226960 0.672703365
#> [Release[3]]-[Release[2]] 27 0.013641984 0.3457665 -0.549363302
#> [Release[4]]-[Release[3]] 21 0.035302589 0.6952949 0.482883828
#> [Release[5]]-[Release[4]] 9 0.001704290 0.0119504 -0.001229736
#> intercept.stderr intercept.pvalue slope.estimate
#> [Release[2]]-[Release[1]] 1.4514313 0.6468799 -0.024457762
#> [Release[3]]-[Release[2]] 0.9049448 0.5492800 0.019082885
#> [Release[4]]-[Release[3]] 0.4144467 0.2583869 -0.013385026
#> [Release[5]]-[Release[4]] 0.3895236 0.9975692 -0.002304159
#> slope.stderr slope.pvalue skewness kurtosis
#> [Release[2]]-[Release[1]] 0.05182753 0.6409299 -0.5542991 0.7361698
#> [Release[3]]-[Release[2]] 0.03245283 0.5617942 -0.9471668 1.3265725
#> [Release[4]]-[Release[3]] 0.01605221 0.4147298 0.4652984 -0.1666573
#> [Release[5]]-[Release[4]] 0.02107760 0.9160182 -0.7282686 -0.4068540
#> JarqueBera.value JarqueBera.pvalue BreuschPagan.R2
#> [Release[2]]-[Release[1]] 1.9923030 0.36929795 0.00661689
#> [Release[3]]-[Release[2]] 5.7939858 0.05518893 0.03750373
#> [Release[4]]-[Release[3]] 0.7448209 0.68907136 0.01951256
#> [Release[5]]-[Release[4]] 0.7623435 0.68306055 0.01512203
#> BreuschPagan.value BreuschPagan.pvalue White.R2
#> [Release[2]]-[Release[1]] 0.1731851 0.6807117 0.06598575
#> [Release[3]]-[Release[2]] 0.9741268 0.3331098 0.05790368
#> [Release[4]]-[Release[3]] 0.3781167 0.5459076 0.02662701
#> [Release[5]]-[Release[4]] 0.1074795 0.7526197 0.02307918
#> White.value White.pvalue arch.R2 arch.value
#> [Release[2]]-[Release[1]] 1.8476009 0.3970074 0.027125777 0.73239597
#> [Release[3]]-[Release[2]] 1.5633995 0.4576275 0.009572775 0.24889216
#> [Release[4]]-[Release[3]] 0.5591672 0.7560985 0.089050061 1.78100121
#> [Release[5]]-[Release[4]] 0.2077126 0.9013548 0.009981670 0.07985336
#> arch.pvalue
#> [Release[2]]-[Release[1]] 0.3921077
#> [Release[3]]-[Release[2]] 0.6178562
#> [Release[4]]-[Release[3]] 0.1820264
#> [Release[5]]-[Release[4]] 0.7774962