Add standard period variables to an experience dataset

Add underwriting, calendar, and development period variables to an experience dataset using standard column conventions for loss ratio analysis.

The function detects the presence of key source columns such as uym, cym, and elap_m, and derives additional period variables when possible.

Usage

add_experience_period(df)

Arguments

df

A data.frame containing period variables such as uym, cym, and elap_m.

Value

A data.frame (or tibble/data.table depending on input) with additional period variables.

Details

The following variables are added when the required source columns exist:

Underwriting period (from uym):

• uy : underwriting year

• uyh : underwriting half-year

• uyq : underwriting quarter

Calendar period (from cym):

• cy : calendar year

• cyh : calendar half-year

• cyq : calendar quarter

Elapsed period:

• elap_y is derived from elap_m as yearly development index, where months 1 to 12 map to 1, 13 to 24 map to 2, and so on.

• elap_h is derived from uym and cym using calendar half-year boundaries. For example, contracts issued in January to June are aligned to the same first development half-year block, and the next calendar half-year becomes development half-year 2.

• elap_q is derived from uym and cym using calendar quarter boundaries. For example, contracts issued in January to March are aligned to the same first development quarter block, and the next calendar quarter becomes development quarter 2.

Therefore, elap_h and elap_q are not simple grouped versions of elap_m; they are aligned to calendar half-year and quarter boundaries so that underwriting cohorts such as Q1, Q2, H1, and H2 are compared consistently on the same cumulative development basis.

Newly created columns are inserted before their corresponding base columns.

Examples

if (FALSE) { # \dontrun{
df <- data.frame(
  uym  = as.Date("2023-01-01") + 0:5 * 30,
  cym  = as.Date("2023-01-01") + 0:5 * 30,
  elap_m = 1:6
)

df2 <- add_experience_period(df)
head(df2)
} # }