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. agecoh 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 ageperiodcohort model and the extended chainladder model. Biometrika 95, 987991. 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 "policyyear" forecast$response.forecast.coh # forecast # coh_2 91718.82 # coh_3 464661.38 # coh_4 704591.94 # coh_5 1025337.23 # coh_6 1503253.81 # coh_7 2330768.44 # coh_8 4115906.56 # coh_9 4257958.30 # coh_10 4567231.84 # forecasts of "cashflow" forecast$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