data.loss.XL {apc}R Documentation

US Casualty data, XL Group

Description

Function that organises US Casualty data from XL Group in apc.data.list format.

The data set is taken from table 1.1 Kuang and Nielsen (2018). Data are for US Ccsualty data from the XL Group. They are gross paid and reported loss and allocated loss adjustment expense in 1000 USD.

The data set is in "CL"-format.

Usage

data.loss.XL

Value

The value is a list in apc.data.list format.

response

matrix of paid amounts, incremental

dose

NULL.

data.format

logical. Equal to "CL".

age1

numeric. Equal to 1.

per1

NULL. Not needed when data.format="CL"

coh1

numeric. Equal to 1997.

unit

numeric. Equal to 1997.

per.zero

NULL. Not needed when data.format="CL"

per.max

NULL. Not needed when data.format="CL"

time.adjust

-1996. Thus age=1 in cohort=1997 corresponds to period=1997+1997-1+(-1996)=1997.

label

character. "loss, US casualty, XL Group".

Author(s)

Bent Nielsen <bent.nielsen@nuffield.ox.ac.uk> 10 Mar 2018

Source

Table 1.1 of Kuang and Nielsen (2018) and in turn download: xls file from XL Group files.

References

Kuang, D, Nielsen B (2018) Generalized log-normal chain-ladder. mimeo Nuffield Collge.

See Also

General description of apc.data.list format.

Examples

#########################
##	It is convenient to construct a data variable for paid data

data	<- data.loss.XL()
##	To see the content of the data
data

#########################
#	Get deviance table.
#	reproduce Table 4.1 in Kuang and Nielsen (2018).

apc.fit.table(data,"log.normal.response")
apc.fit.table(data,"log.normal.response",model.design.reference="AC")

#########################
#	> apc.fit.table(data,"log.normal.response")
#	     -2logL df.residual LR vs.APC df vs.APC prob(>chi_sq) F vs.APC prob(>F)     aic
#	APC 170.003         153       NaN       NaN           NaN      NaN      NaN 286.003
#	AP  243.531         171    73.527        18         0.000    3.564    0.000 323.531
#	AC  179.873         171     9.869        18         0.936    0.409    0.984 259.873
#	PC  633.432         171   463.428        18         0.000   68.736    0.000 713.432
#	Ad  258.570         189    88.567        36         0.000    2.230    0.000 302.570
#	Pd  643.892         189   473.888        36         0.000   36.340    0.000 687.892
#	Cd  649.142         189   479.139        36         0.000   37.368    0.000 693.142
#	A   357.359         190   187.355        37         0.000    5.956    0.000 399.359
#	P   644.176         190   474.172        37         0.000   35.412    0.000 686.176
#	C   672.392         190   502.388        37         0.000   41.099    0.000 714.392
#	t   664.488         207   494.484        54         0.000   27.015    0.000 672.488
#	tA  681.993         208   511.989        55         0.000   29.072    0.000 687.993
#	tP  664.746         208   494.742        55         0.000   26.560    0.000 670.746
#	tC  686.181         208   516.178        55         0.000   29.713    0.000 692.181
#	1   690.399         209   520.396        56         0.000   29.830    0.000 694.399
#
#	> apc.fit.table(data,"log.normal.response",model.design.reference="AC")
#	    -2logL df.residual LR vs.AC df vs.AC prob(>chi_sq) F vs.AC prob(>F)     aic
#	AC 179.873         171      NaN      NaN           NaN     NaN      NaN 259.873
#	Ad 258.570         189   78.698       18             0   4.319        0 302.570
#	Cd 649.142         189  469.269       18             0  79.257        0 693.142
#	A  357.359         190  177.486       19             0  11.955        0 399.359
#	C  672.392         190  492.519       19             0  84.930        0 714.392
#	t  664.488         207  484.615       36             0  42.993        0 672.488
#	tA 681.993         208  502.120       37             0  45.869        0 687.993
#	tC 686.181         208  506.308       37             0  46.886        0 692.181
#	1  690.399         209  510.526       38             0  46.670        0 694.399
 	
 
#########################
#	Fit log normal chain-ladder model
#	reproduce Table 4.2 in Kuang and Nielsen (2018).

fit.ac	<- apc.fit.model(data,"log.normal.response","AC")
id.ac	<- apc.identify(fit.ac)
id.ac$coefficients.dif
fit.ac$s2		
fit.ac$RSS

#########################
#	> id.ac$coefficients.dif
#	                  Estimate Std. Error     t value     Pr(>|t|)
#	level          7.660055032  0.1377951 55.59016605 0.000000e+00
#	D_age_1998     2.272100342  0.1335080 17.01846386 5.992216e-65
#	D_age_1999     0.932530550  0.1362610  6.84370899 7.716860e-12
#	D_age_2000     0.235606356  0.1398301  1.68494782 9.199864e-02
#	D_age_2001     0.088886609  0.1438733  0.61781154 5.366996e-01
#	D_age_2002    -0.176044303  0.1483681 -1.18653717 2.354102e-01
#	D_age_2003    -0.144445459  0.1533567 -0.94189218 3.462478e-01
#	D_age_2004    -0.427608601  0.1589136 -2.69082462 7.127565e-03
#	D_age_2005    -0.300527594  0.1651428 -1.81980421 6.878883e-02
#	D_age_2006    -0.399729999  0.1721838 -2.32153023 2.025824e-02
#	D_age_2007    -0.189656058  0.1802245 -1.05233225 2.926471e-01
#	D_age_2008    -0.242063670  0.1895226 -1.27722853 2.015216e-01
#	D_age_2009    -0.260459607  0.2004421 -1.29942545 1.937980e-01
#	D_age_2010    -0.555317528  0.2135164 -2.60081872 9.300158e-03
#	D_age_2011    -0.303234088  0.2295651 -1.32090683 1.865324e-01
#	D_age_2012     0.405830766  0.2499291  1.62378389 1.044219e-01
#	D_age_2013    -0.895278068  0.2769988 -3.23206421 1.228994e-03
#	D_age_2014     0.116668873  0.3156054  0.36966685 7.116307e-01
#	D_age_2015    -0.383048241  0.3777268 -1.01408813 3.105407e-01
#	D_age_2016    -0.273419402  0.5083832 -0.53782152 5.907003e-01
#	D_cohort_1998  0.288755900  0.1335080  2.16283663 3.055375e-02
#	D_cohort_1999  0.163424236  0.1362610  1.19934721 2.303930e-01
#	D_cohort_2000 -0.264981486  0.1398301 -1.89502518 5.808907e-02
#	D_cohort_2001  0.149829430  0.1438733  1.04139815 2.976908e-01
#	D_cohort_2002 -0.374386828  0.1483681 -2.52336417 1.162380e-02
#	D_cohort_2003 -0.198735893  0.1533567 -1.29590632 1.950078e-01
#	D_cohort_2004 -0.008807130  0.1589136 -0.05542087 9.558032e-01
#	D_cohort_2005 -0.005337953  0.1651428 -0.03232325 9.742143e-01
#	D_cohort_2006 -0.132272851  0.1721838 -0.76820710 4.423642e-01
#	D_cohort_2007 -0.021862643  0.1802245 -0.12130783 9.034472e-01
#	D_cohort_2008 -0.472602270  0.1895226 -2.49364600 1.264386e-02
#	D_cohort_2009 -0.437572798  0.2004421 -2.18303804 2.903301e-02
#	D_cohort_2010  0.295511564  0.2135164  1.38402260 1.663515e-01
#	D_cohort_2011  0.310545832  0.2295651  1.35275725 1.761332e-01
#	D_cohort_2012 -0.268692406  0.2499291 -1.07507473 2.823413e-01
#	D_cohort_2013  0.142131410  0.2769988  0.51311192 6.078730e-01
#	D_cohort_2014  0.201777590  0.3156054  0.63933494 5.226051e-01
#	D_cohort_2015 -0.092672697  0.3777268 -0.24534320 8.061907e-01
#	D_cohort_2016  0.872997251  0.5083832  1.71720334 8.594203e-02	
#	> fit.ac$s2	
#	[1] 0.1693316
#	> fit.ac$RSS
#	[1] 28.9557
#	> fit.ac$RSS



























[Package apc version 1.3.3 Index]