Get discount factor

This function calculates the discount factor based on discount rate.

Usage

get_discount_factor(discount_rate, n_years, discount_shape = "exponential")

Arguments

discount_rate: Numeric value showing the discount rate for future years.
n_years: Numeric value or numeric vector specifying the number of years elapsed for which the discount factor is to be calculated. One factor is returned per entered value. The year 0, i.e. the present, gets a factor of 1 (no discounting). Note that this differs from the argument of the same name in monetize(), which is the time horizon: monetize() calls this function with each single year from 0 to that horizon.
discount_shape: String referring to the assumed equation for the discount factor. By default: "exponential". Otherwise: "hyperbolic_harvey_1986" or "hyperbolic_mazur_1987".

Value

This function returns the numeric discount factor(s), one per value entered in n_years.

Details

Methodology

This function is called inside monetize().

One of the following three discount shapes can be selected:

Exponential (Frederick et al. 2002; HM Treasury 2026)
Hyperbolic as Harvey (1986)
Hyperbolic as Mazur (1987)

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

Monetization

References

Frederick S, Loewenstein G, O'Donoghue T (2002). “Time Discounting and Time Preference: A Critical Review.” Journal of Economic Literature, 40(2), 351–401. doi:10.1257/002205102320161311 .

Harvey CM (1986). “Value Functions for Infinite-Period Planning.” Management Science, 32(9), 1123–1139. doi:10.1287/mnsc.32.9.1123 .

HM Treasury (2026). “Discounting: Green Book supplementary guidance.” HM Treasury, London, UK. https://www.gov.uk/government/publications/green-book-supplementary-guidance-discounting.

Mazur JE (1987). “An adjusting procedure for studying delayed reinforcement.” In Commons ML, Mazur JE, Nevin JA, Rachlin H (eds.), Quantitative Analyses of Behavior: Volume V. The Effect of Delay and of Intervening Events on Reinforcement Value, 55–73. Lawrence Erlbaum Associates, Hillsdale, NJ. ISBN 0-89859-800-1.

Author

Alberto Castro & Axel Luyten

Examples

# Goal: discount factor after a given number of years
get_discount_factor(
  discount_rate = 0.07,
  n_years = 5
 )
#> [1] 0.7129862

# Goal: discount factor for each year of a time horizon
get_discount_factor(
  discount_rate = 0.07,
  n_years = 0:5
 )
#> [1] 1.0000000 0.9345794 0.8734387 0.8162979 0.7628952 0.7129862