Linear regression model of a latter vintage (L) 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)
slope_and_drift(vintages$diagonal_view)
#> N R2 F intercept.estimate intercept.stderr
#> Release[2] 29 0.9939361 4425.602 0.15027633 0.6926224
#> Release[3] 26 0.9985866 16956.903 -0.25206122 0.3298895
#> Release[4] 17 0.9992438 19821.412 0.23581438 0.2107640
#> Release[5] 14 0.9997989 59650.136 -0.04437905 0.1026261
#> intercept.pvalue slope.estimate slope.stderr slope.pvalue skewness
#> Release[2] 0.8298651 0.9961734 0.014974377 0.8002403 -0.3722054
#> Release[3] 0.4522681 1.0026125 0.007699452 0.7373301 -0.6780136
#> Release[4] 0.2808000 1.0081042 0.007160414 0.2754838 -0.2080953
#> Release[5] 0.6730950 0.9986551 0.004088931 0.7478881 -0.3615824
#> kurtosis JarqueBera.value JarqueBera.pvalue BreuschPagan.R2
#> Release[2] 0.89969211 1.5908588 0.4513874 0.003804105
#> Release[3] -0.41506612 2.0948851 0.3508338 0.004235920
#> Release[4] -1.00251497 0.7855006 0.6751973 0.075373274
#> Release[5] -0.05842755 0.2851232 0.8671342 0.113710532
#> BreuschPagan.value BreuschPagan.pvalue White.R2 White.value
#> Release[2] 0.1031030 0.7506095 0.02828903 0.8203818
#> Release[3] 0.1020945 0.7520952 0.08563116 2.2264103
#> Release[4] 1.2227627 0.2862397 0.10342178 1.7581703
#> Release[5] 1.5395945 0.2383890 0.12346070 1.7284498
#> White.pvalue arch.R2 arch.value arch.pvalue
#> Release[2] 0.6635236 0.0746503261 2.090209131 0.1482458
#> Release[3] 0.3285044 0.0450459388 1.126148471 0.2885984
#> Release[4] 0.4151625 0.0002702476 0.004323962 0.9475714
#> Release[5] 0.4213780 0.0145449596 0.189084475 0.6636797