apc.forecast.apc {apc} | R Documentation |
Forecast models with APC structure.
Description
Computes forecasts for a model with APC structure. Forecasts of the linear predictor are given for all models. This is done for the triangle which shares age and cohort indices with the data.
Usage
apc.forecast.apc(apc.fit,extrapolation.type="I0",
suppress.warning=TRUE)
Arguments
apc.fit |
List. Output from |
extrapolation.type |
Character. Choices for extrapolating the differenced period parameter ("Delta.beta_per"). Default is "I0".
All methods are invariant to ad hoc identification of the implied period time effect, by following the ideas put forward in Kuang, Nielsen and Nielsen (2008b). |
suppress.warning |
Logical. If true, suppresses warnings from |
Details
The example below is based on the smaller data reserving sets
data.loss.TA
.
Value
linear.predictors.forecast |
Vector. Linear predictors for forecast area. |
index.trap.J |
Matrix. age-coh coordinates for vector. Similar structure to
|
trap.response.forecast |
Matrix. Includes data and point forecasts. Forecasts in lower right triangle. Trapezoid format. |
response.forecast.cell |
Matrix. 4 columns.
1: Point forecasts.
2: corresponding forecast standard errors
3: process standard errors
4: estimation standard errors
Note that the square of column 2 equals the sums of squares of columns 3 and 4
Note that |
response.forecast.age |
Same as |
response.forecast.per |
Same as |
response.forecast.coh |
Same as |
response.forecast.all |
Same as |
xi.per.dd.extrapolated |
The extrapolated double differences. |
xi.extrapolated |
The extrapolated parameters. |
Author(s)
Bent Nielsen <bent.nielsen@nuffield.ox.ac.uk> 10 Sep 2016
References
Kuang, D., Nielsen, B. and Nielsen, J.P. (2008b) Forecasting with the age-period-cohort model and the extended chain-ladder model. Biometrika 95, 987-991. Download: Article; Earlier version Nuffield DP.
See Also
The example below uses Taylor and Ashe reserving see data.loss.TA
Examples
#####################
# EXAMPLE with reserving data: data.loss.TA()
data <- data.loss.TA()
fit.apc <- apc.fit.model(data,"poisson.response","APC")
forecast <- apc.forecast.apc(fit.apc)
# forecasts by "policy-year"
forecast$response.forecast.coh
# forecast
# coh_2 91718.82
# coh_3 464661.38
# coh_4 704591.94
# coh_5 1025337.23
# coh_6 1503253.81
# coh_7 2330768.44
# coh_8 4115906.56
# coh_9 4257958.30
# coh_10 4567231.84
# forecasts of "cash-flow"
forecast$response.forecast.per
# forecast
# per_11 5274762.58
# per_12 4213526.23
# per_13 3188451.80
# per_14 2210649.45
# per_15 1644203.06
# per_16 1236495.32
# per_17 764552.75
# per_18 444205.71
# per_19 84581.44
# forecast of "total reserve"
forecast$response.forecast.all
# forecast
# all 19061428