apc.forecast.apc {apc} | R Documentation |
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.
apc.forecast.apc(apc.fit,extrapolation.type="I0", suppress.warning=TRUE)
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 |
The example below is based on the smaller data reserving sets
data.loss.TA
.
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. |
Bent Nielsen <bent.nielsen@nuffield.ox.ac.uk> 10 Sep 2016
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.
The example below uses Taylor and Ashe reserving see data.loss.TA
##################### # 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