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Last data update: 2014.03.03

R: True value for simulation

spfrontier.true.value

R Documentation

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.

Examples

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)