spfrontier (version 0.2.5)

spfrontier.true.value: True value for simulation

Description

spfrontier.true.value returns true parameter values for a simulation process

ezsimspfrontier tests estimators of a spatial stochastic frontier model with different parameters

Usage

spfrontier.true.value()
ezsimspfrontier(
  runs,
  params,
  inefficiency = "half-normal",
  logging = "info",
  control = list()
)

Arguments

runs

a number of simulated samples

params

a set with parameters to be used in simulation.

inefficiency

sets the distribution for inefficiency error component. Possible values are 'half-normal' (for half-normal distribution) and 'truncated' (for truncated normal distribution). By default set to 'half-normal'. See references for explanations

logging

an optional level of logging. Possible values are 'quiet','warn','info','debug'. By default set to quiet.

control

an optional list of control parameters for simulation process. Currently the procedure supports: ignoreWy (TRUE/FALSE) - the spatial contiguity matrix for a dependent variable is not provided to spfrontier estimator (but used in DGP) ignoreWv (TRUE/FALSE) - the spatial contiguity matrix for a symmetric error term is not provided to spfrontier estimator (but used in DGP) ignoreWu (TRUE/FALSE) - the spatial contiguity matrix for a inefficiency error term is not provided to spfrontier estimator (but used in DGP) parallel (TRUE/FALSE) - whether to use parallel computer seed - a state for random number generation in R. If NULL (default), the initial state is random. See set.seed for details. auto_save - saves intermediate results to files. See ezsim for details.

Details

The spfrontier.true.value function should notbe used directly, it is exported for supporting ezsim

The ezsimspfrontier function executes multiple calls of the spfrontier estimator on a simulated data set, generated on the base of provided parameters. The resulting estimates can be analysed for biasedness, efficiency, etc.

See Also

ezsim

Examples

# NOT RUN {
params000 <- list(n=c(50, 100),beta0=5,
                 beta1=10,
                 beta2=1,
                 sigmaV=0.5, 
                 sigmaU=2.5)
ctrl <- list(seed=999, cores=1)
res000 <- ezsimspfrontier(2, params = params000,  
                 inefficiency = "half-normal",
                 logging = "info", 
                 control=ctrl)
summary(res000)
# }