rmdcev (version 1.2.0)

mdcev.sim: mdcev.sim

Description

Simulate welfare or demand for MDCEV model

Usage

mdcev.sim(
  df_indiv,
  df_common,
  sim_options,
  sim_type = c("welfare", "demand"),
  nerrs = 30,
  cond_error = 1,
  draw_mlhs = 1,
  algo_gen = NULL,
  tol = 1e-20,
  max_loop = 999,
  suppressTime = FALSE,
  stan_seed = 3,
  ...
)
# S3 method for mdcev.sim
print(
  x,
  digits = max(3, getOption("digits") - 3),
  width = getOption("width"),
  ...
)
# S3 method for mdcev.sim
summary(object, ci = 0.95, ...)
# S3 method for summary.mdcev.sim
print(
  x,
  digits = max(3, getOption("digits") - 2),
  width = getOption("width"),
  ...
)

Arguments

df_indiv

Prepared individual level data from PrepareSimulationData

df_common

Prepared common data from PrepareSimulationData

sim_options

Prepared simulation options from PrepareSimulationData

sim_type

Either "welfare" or "demand"

nerrs

Number of error draws for welfare analysis

cond_error

Choose whether to draw errors conditional on actual demand or not. Conditional error draws (=1) or unconditional error draws.

draw_mlhs

Generate draws using Modified Latin Hypercube Sampling algorithm (=1) or uniform (=0)

algo_gen

Type of algorithm for simulation. algo_gen = 0 for Hybrid Approach (i.e. constant alphas, only hybrid models) algo_gen = 1 for General approach (i.e. heterogeneous alpha's, all models)

tol

Tolerance level for simulations if using general approach

max_loop

maximum number of loops for simulations if using general approach

suppressTime

Suppress simulation time calculation

stan_seed

Seed for pseudo-random number generator get_rng see help(get_rng, package = "rstan")

...

Additional parameters to pass to mdcev.sim

x, object

an object of class `mdcev.sim`

digits

the number of digits,

width

the width of the printing,

ci

choose confidence interval for simulations. Default is 95 percent.

Value

a object of class mdcev.sim which contains a list for each individual holding either 1) nsims x npols matrix of welfare changes if welfare is being simulated or 2) nsims number of lists of npols x # alternatives matrix of Marshallian demands is demand is being simulated.

See Also

[mdcev()] for the estimation of mdcev models.

Examples

# NOT RUN {
data(data_rec, package = "rmdcev")

data_rec <- mdcev.data(data_rec, subset = id <= 500, id.var = "id",
                alt.var = "alt", choice = "quant")

mdcev_est <- mdcev( ~ 0, data = data_rec,
               model = "hybrid0", algorithm = "MLE",
               std_errors = "mvn")

policies <- CreateBlankPolicies(npols = 2,
             mdcev_est,
             price_change_only = TRUE)

df_sim <- PrepareSimulationData(mdcev_est, policies)

wtp <- mdcev.sim(df_sim$df_indiv,
                 df_common = df_sim$df_common,
                 sim_options = df_sim$sim_options,
                 cond_err = 1, nerrs = 5, sim_type = "welfare")
# }