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Future population simulation

Source: R/popsim.R
generate_population.Rd

Simulate future age-specific population given a starting population and models for fertility, mortality, and migration. If any model is NULL, it is assumed there are no future births, deaths or net migrants, respectively. This is an experimental function and has not been thoroughly tested. The simulation follows demography::pop.sim(), as described in Hyndman and Booth (2008).

Usage

generate_population(
  starting_population,
  mortality_model = NULL,
  fertility_model = NULL,
  migration_model = NULL,
  h = 10,
  n_reps = 1000,
  female = NULL
)

Arguments

starting_population

A vital object with the age-sex-specific starting population.

mortality_model

A mable object containing an age-sex-specific model for mortality rates, trained on data up to the year of the starting population. If NULL, there are zero future deaths.

fertility_model

A mable object containing an age-specific model for fertility rates, trained on data up to the year of the starting population. If NULL, there are zero future births.

migration_model

A mable object containing an age-sex-specific model for net migration numbers, trained on data up to the year of the starting population. Net migrants are indexed by age at the end of the year, as returned by net_migration(). If NULL, there are zero future net migrants.

h

The forecast horizon equal to the number of years to simulate into the future.

n_reps

The number of replicates to simulate.

female

A character string giving the name used for females in the sex variable of the starting_population. This is needed when computing births from the fertility rates. If missing, the function will try to identify the most likely value automatically.

Value

A vital object containing the simulated future population.

References

Hyndman and Booth (2008) Stochastic population forecasts using functional data models for mortality, fertility and migration. International Journal of Forecasting, 24(3), 323-342.

Examples

# Norwegian data, with ages above 100 combined
nor <- norway_mortality |>
  dplyr::filter(Sex != "Total") |>
  collapse_ages(max_age = 100)
# Models trained on data up to the year of the starting population
mortality <- nor |>
  dplyr::filter(Year > 2010) |>
  model(fmean = FMEAN(log(Mortality)))
fertility <- norway_fertility |>
  dplyr::filter(Year > 2010) |>
  model(fmean = FMEAN(Fertility))
migration <- net_migration(nor, norway_births) |>
  dplyr::filter(Year > 2010) |>
  model(fmean = FMEAN(NetMigration))
# Simulate 5 future populations for 3 years, starting from the final year of data
generate_population(
  starting_population = nor |> dplyr::filter(Year == max(Year)),
  mortality_model = mortality,
  fertility_model = fertility,
  migration_model = migration,
  h = 3,
  n_reps = 5
)
#> # A vital: 3,030 x 5 [1Y]
#> # Key:     Age x (Sex, .rep) [101 x 10]
#>     Year   Age Sex    .rep  Population
#>    <int> <int> <chr>  <chr>      <dbl>
#>  1  2024     0 Female 1          28791
#>  2  2024     1 Female 1          25636
#>  3  2024     2 Female 1          28528
#>  4  2024     3 Female 1          26908
#>  5  2024     4 Female 1          27810
#>  6  2024     5 Female 1          27863
#>  7  2024     6 Female 1          28828
#>  8  2024     7 Female 1          29870
#>  9  2024     8 Female 1          30189
#> 10  2024     9 Female 1          30424
#> # ℹ 3,020 more rows