mdcev: mdcev

In rmdcev: Kuhn-Tucker and Multiple Discrete-Continuous Extreme Value Models

mdcev

Description

Fit a MDCEV model using MLE or Bayes

Usage

mdcev(
  formula = NULL,
  data,
  weights = NULL,
  model = c("alpha", "gamma", "hybrid", "hybrid0", "kt_ee"),
  n_classes = 1,
  fixed_scale1 = 0,
  single_scale = 0,
  trunc_data = 0,
  psi_ascs = NULL,
  gamma_ascs = 1,
  seed = "123",
  max_iterations = 2000,
  jacobian_analytical_grad = 1,
  initial.parameters = "random",
  hessian = TRUE,
  algorithm = c("MLE", "Bayes"),
  flat_priors = NULL,
  print_iterations = TRUE,
  prior_psi_sd = 10,
  prior_gamma_sd = 10,
  prior_phi_sd = 10,
  prior_alpha_shape = 1,
  prior_scale_sd = 1,
  prior_delta_sd = 10,
  gamma_nonrandom = 0,
  alpha_nonrandom = 0,
  std_errors = "deltamethod",
  n_draws = 50,
  keep_loglik = 0,
  random_parameters = "fixed",
  show_stan_warnings = TRUE,
  n_iterations = 200,
  n_chains = 4,
  n_cores = 4,
  max_tree_depth = 10,
  adapt_delta = 0.8,
  lkj_shape_prior = 4,
  ...
)

## S3 method for class 'mdcev'
print(
  x,
  digits = max(3, getOption("digits") - 3),
  width = getOption("width"),
  ...
)

## S3 method for class 'mdcev'
summary(object, printCI = FALSE, ...)

## S3 method for class 'summary.mdcev'
print(x, ...)

Arguments

formula	Formula for the model to be estimated. The formula is divided in three parts, separated by the symbol |. The first part is reserved for alternative-specific and individual-specific variables in the psi parameters. Note that alternative-specific constants are handled by the psi_ascs argument. The second part corresponds for individual-specific variables that enter in the probability assignment in models with latent classes. The third part is reserved for the $q_k$ variables included in the $phi_k$ parameters in the KT model specification used in environmental economics model = "kt_ee".
data	The (IxJ) data to be passed to Stan of class mdcev.data including 1) id, 2) alt, 3) choice, 4) price, 5) income, and columns for alternative-specific and individual specific variables. Note: I is number of individuals and J is number of non-numeraire alternatives.
weights	an optional vector of weights. Default to 1.
model	A string indicating which model specification is estimated. The options are "alpha", "gamma", "hybrid" and "hybrid0" for the MDCEV model and "kt_ee" for the environmental economics Kuhn-Tucker specification.
n_classes	The number of latent classes. Note that the LC model is automatically estimated as long as the prespecified number of classes is set greater than 1.
fixed_scale1	Whether to fix scale at 1.
single_scale	For lc models, whether to estimate a single scale parameter
trunc_data	Whether the estimation should be adjusted for truncation of non-numeraire alternatives. This option is useful if the data only includes individuals with positive non-numeraire consumption levels such as recreation data collected on-site. To account for the truncation of consumption, the likelihood is normalized by one minus the likelihood of observing zero consumption for all non-numeraire alternatives (i.e. likelihood of positive consumption) following Englin, Boxall and Watson (1998) and von Haefen (2003).
psi_ascs	Whether to include alternative-specific psi parameters. The first alternative is used as the reference category. Only specify to 1 for MDCEV models.
gamma_ascs	Indicator to include alternative-specific gammas parameters.
seed	Random seed.
max_iterations	Maximum number of iterations in MLE.
jacobian_analytical_grad	indicator whether to use analytical gradient method for Jacobian (=1) or numerical gradient method (=0). For "kt_ee" model only,
initial.parameters	The default for fixed and random parameter specifications is to use random starting values. (except for the scale parameter with a starting value set to 1). For LC models, the default is to use slightly adjusted MLE point estimates from the single class model. Initial parameter values should be included in a named list. For example, the LC "hybrid" specification initial parameters can be specified as: initial.parameters = list(psi = array(0, dim = c(K, num_psi)), gamma = array(1, dim = c(K, num_alt)), alpha = array(0.5, dim = c(K, 0)), scale = array(1, dim = c(K))) where K is the number of classes (i.e. K = 1 is used for single class models), num_psi is number of psi parameters, and num_alt is number of non-numeraire alternatives.
hessian	Whether to keep the Hessian matrix
algorithm	Either "Bayes" for Bayes or "MLE" for maximum likelihood estimation.
flat_priors	indicator if completely uninformative priors should be specified. Defaults to 1 if MLE used and 0 if Bayes used. If using MLE and set flat_priors = 0, penalized MLE is used and the optimizing objective is augmented with the priors.
print_iterations	Whether to print iteration information
prior_psi_sd	standard deviation for normal prior with mean 0.
prior_gamma_sd	standard deviation for half-normal prior with mean 1.
prior_phi_sd	standard deviation for normal prior with mean 0.
prior_alpha_shape	shape parameter for beta distribution.
prior_scale_sd	standard deviation for half-normal prior with mean 0.
prior_delta_sd	standard deviation for normal prior with mean 0.
gamma_nonrandom	indicator set to 1 if gamma parameters should not be random (i.e. no standard deviation).
alpha_nonrandom	indicator set to 1 if alpha parameters should not be random (i.e. no standard deviation).
std_errors	Compute standard errors using the delta method ("deltamethod") or multivariate normal draws ("mvn"). The default is "deltamethod". Note that mvn parameter draws should be used to incorporate parameter uncertainty for demand and welfare simulation. For maximum likelihood estimation only.
n_draws	The number of multivariate normal draws for standard error calculations if "mvn" is specified.
keep_loglik	Whether to keep the log_lik calculations
random_parameters	The form of the covariance matrix for Bayes. Can be 'fixed' for no random parameters, 'uncorr' for uncorrelated random parameters, or 'corr' for correlated random parameters.
show_stan_warnings	Whether to show warnings from Stan.
n_iterations	The number of iterations to use in Bayesian estimation. The default is for the number of iterations to be split evenly between warmup and posterior draws. The number of warmup draws can be directly controlled using the warmup argument (see rstan::sampling).
n_chains	The number of independent Markov chains in Bayesian estimation.
n_cores	The number of cores used to execute the Markov chains in parallel in Bayesian estimation. Can set using options(mc.cores = parallel::detectCores()).
max_tree_depth	https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded
adapt_delta	https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
lkj_shape_prior	Prior for Cholesky matrix
...	Additional parameters to pass on to rstan::stan and rstan::sampling.
x, object	an object of class 'mdcev'
digits	the number of digits,
width	the width of the printing,
printCI	set to TRUE to print 95% confidence intervals

Value

A object of class mdcev

Examples

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")

rmdcev documentation built on March 31, 2023, 6:49 p.m.