Predict the Reserve using Chain Ladder Plus Models
Source:R/predictclmplusmodel.R
predict.clmplusmodel.RdPredict the lower triangle with a clmplus model.
Arguments
- object
clmplusmodel, Model to predict from.- gk.fc.model
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.- ckj.fc.model
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.- gk.order
integer, order of the arima model with drift for the accident year effect extrapolation. Default to (1,1,0).- ckj.order
integer, order of the arima model with drift for the calendar year effect extrapolation. Default to (0,1,0).- forecasting_horizon
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.
Value
Returns the following output:
- reserve
numericThe reserve for each accident period.- ultimate_cost
numericThe ultimate cost for each accident period.- full_triangle
matrix arrayThe complete run-off triangle of cumulative payments, it includes the (input) upper triangle and the predicted (output) lower triangle.- lower_triangle
matrix arrayThe predicted lower triangle of cumulative payments.- development_factors_predicted
matrix arrayThe predicted lower triangle of the extrapolated development factors.- apc_output
listThe 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.