R4GoodPersonalFinances (version 1.2.0)

simulate_scenario: Simulate a scenario of household lifetime finances

Description

The function simulates a scenario of household lifetime finances and returns a tibble with nested columns. By default no Monte Carlo samples are generated, and only single sample based on portfolio expected returns are returned with column sample=0. If the additional Monte Carlo samples are generated, they have consecutive IDs starting from 1 in the sample column.

Usage

simulate_scenario(
  household,
  portfolio,
  scenario_id = "default",
  current_date = get_current_date(),
  monte_carlo_samples = NULL,
  seeds = NULL,
  use_cache = FALSE,
  auto_parallel = FALSE,
  debug = FALSE,
  ...
)

Value

A tibble with nested columns including:

• scenario_id - (character) ID of the scenario

• sample - (integer) ID of the Monte Carlo sample

• index - (integer) year index starting from 0

• years_left - (integer) years left to the end of the household lifespan

• date - (date) date after index years from the current date

• year - (integer) calendar year

• survival_prob - (double) survival probability of the household

• members - (nested tibble) data of each member in the household

• income - (nested tibble) income streams

• total_income - (double) total income of the household from all income streams

• spending - (nested tibble) non-discretionary spending streams

• nondiscretionary_spending - (double) total non-discretionary spending of the household from all non-discretionary spending streams

• human_capital - (double) human capital of the household

• liabilities - (double) liabilities of the household

• portfolio - (nested tibble) state of investment portfolio

• financial_wealth - (double) financial wealth of the household at the beginning of the year

• net_worth - (double) net worth of the household

• discretionary_spending - (double) optimal discretionary spending of the household

• total_spending - (double) total spending of the household (discretionary + non-discretionary)

• financial_wealth_end - (double) financial wealth of the household at the end of the year

• risk_tolerance - (double) risk tolerance of the household

• smooth_consumption_preference - (double) smooth consumption preference of the household

• consumption_impatience_preference - (double) consumption impatience preference of the household

• time_value_discount - (double) time value discount based on consumption impatience of the household

• discretionary_spending_utility - (double) discretionary spending utility of the household based on the smooth consumption preference

• discretionary_spending_utility_weighted - (double) discretionary spending utility of the household weighted by survival probability and time value discount.

Arguments

household

An R6 object of class Household.

portfolio

A nested tibble of class Portfolio.

scenario_id

A character. ID of the scenario.

current_date

A character. Current date in the format YYYY-MM-DD. By default, it is the output of get_current_date().

monte_carlo_samples

An integer. Number of Monte Carlo samples. If NULL (default), no Monte Carlo samples are generated.

seeds

An integer or integer vector. If integer vector, it is a vector of random seeds for the random number generator used to generate random portfolio returns for each Monte Carlo sample. If NULL (default), random seed is generated automatically. If a single integer is provided, it is used to generate a vector of random seeds for each Monte Carlo sample.

use_cache

A logical. If TRUE, the function uses memoised functions to speed up the simulation. The results are cached in the folder set by set_cache().

auto_parallel

A logical. If TRUE, the function automatically detects the number of cores and uses parallel processing to speed up the Monte Carlo simulations. The results are cached in the folder set by set_cache().

debug

A logical. If TRUE, additional information is printed during the simulation.

...

Additional arguments passed to simulation and optimization functions. You can pass a list named opts as parameter to the optimization function to select the optimization algorithm and its parameters. See nloptr::nloptr() and nloptr::nloptr.print.options() for more information.

Examples

if (FALSE) { # interactive()
older_member <- HouseholdMember$new(
  name       = "older",  
  birth_date = "1980-02-15",
  mode       = 80,
  dispersion = 10
)  
household <- Household$new()
household$add_member(older_member)  

household$expected_income <- list(
  "income" = c(
    "members$older$age <= 65 ~ 7000 * 12"
  )
)
household$expected_spending <- list(
  "spending" = c(
    "TRUE ~ 5000 * 12"
  )
)

portfolio <- create_portfolio_template() 
portfolio$accounts$taxable <- c(10000, 30000)
portfolio <- 
  portfolio |> 
  calc_effective_tax_rate(
    tax_rate_ltcg = 0.20, 
    tax_rate_ordinary_income = 0.40
  )

scenario <- 
  simulate_scenario(
   household = household,
   portfolio = portfolio,
   current_date = "2020-07-15"
  )
names(scenario)
}