rpm (version 0.7-3)

simulate.rpm: Simulate a sample of pairs and singles from a Revealed Preference Matchings Model

Description

simulate.rpm simulates a population of the pairs and singles from a Revealed Preference Matchings Model. It is typically based on the estimate from a rpm() call.

Usage

# S3 method for rpm
simulate(
  object,
  nsim = 1,
  seed = NULL,
  ...,
  N = NULL,
  num_women = NULL,
  num_men = NULL,
  pmfW = NULL,
  pmfM = NULL,
  large.population = TRUE,
  num_sampled = NULL,
  bootstrap = FALSE,
  sampling_design = NULL,
  control = control.rpm(),
  verbose = FALSE
)

Value

A list of data.frame, each a simulation from the population.

Arguments

object

list; an object of classrpm that is typically the result of a call to rpm().

nsim

Number of matchings to be randomly drawn from the given model on the set of all matchings / singles.

seed

integer; (Optional) random number seed.

...

Additional arguments, to be passed to lower-level functions.

N

integer; The total population size. This must be set. The number of women and men are derived from the (weighted) data.

num_women

integer; (Optional) The number of women in the population.

num_men

integer; (Optional) The number of men in the population.

pmfW

vector; (Optional) The population proportions of the numbers of women of each type. This should be compatible with the type in the object.

pmfM

vector; (Optional) The population proportions of the numbers of men of each type. This should be compatible with the type in the object.

large.population

logical; If TRUE a large population approximation is used to generate the matchings (rather than the individual level generation of utilities). This is much faster and uses a lot less memory. It is TRUE by default. If used, a sample is drawn rather than the population being returned. The sample size is controlled by num_sampled.

num_sampled

integer; The size of the sample to be drawn. For "stock-stock" sampling this is the number of sampled households. For "stock-flow" it is the number of sampled people. For "census" it is the total population size, N. If NULL the size is the same as the passed fitted object (that is, the original data), although this is only a guess and it should be explicitly set.

bootstrap

logical; If TRUE the original population is sampled from. If FALSE the population underlying the fitted model is sampled from.

sampling_design

string; The name of the sampling protocol used to select the survey data. Valid values are "stock-flow" (individuals are sampled, data contains both singles and couples); "stock-stock" (households are sampled, each household can be a single or a couple); "census" (the sample is a census of the population of people). The final option, the default, is NULL whereby the design is taken from the passed object.

control

A list of control parameters for algorithm tuning. Constructed using control.rpm, which should be consulted for specifics.

verbose

logical; Should verbose messages be printed out.

Details

The function rpm is used to fit a revealed preference model for men and women of certain characteristics (or shared characteristics) of people of the opposite sex. The model assumes a one-to-one stable matching using an observed set of matchings and a set of (possibly dyadic) covariates to estimate the parameters for linear equations of utilities. It does this using an large-population likelihood based on ideas from Dagsvik (2000), Menzel (2015) and Goyal et al (2023).

The model represents the dyadic utility functions as deterministic linear utility functions of dyadic variables. These utility functions are functions of observed characteristics of the women and men. These functions are entered as terms in the function call to rpm. This function simulates from such a model.

References

Goyal, Shuchi; Handcock, Mark S.; Jackson, Heide M.; Rendall, Michael S. and Yeung, Fiona C. (2023). A Practical Revealed Preference Model for Separating Preferences and Availability Effects in Marriage Formation, Journal of the Royal Statistical Society, A. tools:::Rd_expr_doi("10.1093/jrsssa/qnad031")

Dagsvik, John K. (2000) Aggregation in Matching Markets International Economic Review,, Vol. 41, 27-57. JSTOR: https://www.jstor.org/stable/2648822, tools:::Rd_expr_doi("10.1111/1468-2354.00054")

Menzel, Konrad (2015). Large Matching Markets as Two-Sided Demand Systems Econometrica, Vol. 83, No. 3 (May, 2015), 897-941. tools:::Rd_expr_doi("10.3982/ECTA12299")

Examples

library(rpm)
# \donttest{
data(fauxmatching)
fit <- rpm(~match("edu") + WtoM_diff("edu",3),
          Xdata=fauxmatching$Xdata, Zdata=fauxmatching$Zdata,
          X_w="X_w", Z_w="Z_w",
          pair_w="pair_w", pair_id="pair_id", Xid="pid", Zid="pid",
          sampled="sampled")
a <- simulate(fit)
# }