Predict the lower triangle with a clmplus model.
# S3 method for clmplusmodel
predict(
object,
gk.fc.model = "a",
ckj.fc.model = "a",
gk.order = c(1, 1, 0),
ckj.order = c(0, 1, 0),
forecasting_horizon = NULL,
...
)Returns the following output:
numeric The reserve for each accident period.
numeric The ultimate cost for each accident period.
matrix array The complete run-off triangle of cumulative payments, it includes the (input) upper triangle and the predicted (output) lower triangle.
matrix array The predicted lower triangle of cumulative payments.
matrix array The predicted lower triangle of the extrapolated development factors.
list The following output from the age-period-cohort representation: model.fit (fitStMoMo) age-period-cohort model fit.
alphaij (matrix array) predicted claim development.
lower_triangle_apc (matrix array) predicted lower triangle of cumulative payments in age-period-cohort form.
development_factors_apc (matrix array) development factors in age-period-cohort representation.
clmplusmodel, Model to predict from.
character, model to forecast the cohort component for the last accident period. It can be either arima ('a') or linear model ('l'). Disregarded for models that do not have a cohort effect.
character, model to forecast the calendar period effect. It can be either arima ('a') or linear model ('l'). Disregarded for models that do not have a period effect.
integer, order of the arima model with drift for the accident year effect extrapolation. Default to (1,1,0).
integer, order of the arima model with drift for the calendar year effect extrapolation. Default to (0,1,0).
integer, between 1 and the triangle width. Calendar periods ahead for the predictions. Default predictions are to run-off.
Extra arguments to be passed to the predict function.
Pittarello, Gabriele, Munir Hiabu, and Andrés M. Villegas. "Replicating and extending chain ladder via an age-period-cohort structure on the claim development in a run-off triangle." arXiv preprint arXiv:2301.03858 (2023).