simulation: True value for simulation
In spfrontier: Spatial Stochastic Frontier Models

Description Usage Arguments Details See Also Examples

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

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

spfrontier.true.value()

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

`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.

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.

ezsim

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)

Loading required package: moments
Loading required package: tmvtnorm
Loading required package: mvtnorm
Loading required package: Matrix
Loading required package: stats4
Loading required package: gmm
Loading required package: sandwich
Loading required package: spdep
Loading required package: sp

Attaching package: 'spdep'

The following object is masked from 'package:moments':

    geary

Loading required package: ezsim
Thank you for using 'spfrontier'
sh: 1: wc: Permission denied
sh: 1: cannot create /dev/null: Permission denied
Predefined random seed: 999
[1] "Mark =  270"
Start [pid= 14445  n = 50 , run = 1 ]-------------------------> -2.17513749937688
Log-likelihood (true DGP) (pid=14445) = -85.2578166841441
INFO spfrontier (pid=14445) Estimator started
INFO spfrontier (pid=14445) Calculating initial values
INFO calculateInitialValues (pid=14445) Initial values method errorsarlm
INFO spfrontier (pid=14445) Initial values:
     Beta1      Beta2      Beta3     sigmaV     sigmaU 
 1.6063826 10.3300718  1.6485546  0.5442064  2.1509508 
Maximizing -- use negfn and neggr
INFO doTryCatch (pid=14445) Calculating numeric hessian, number of required function calls is  122  ...
INFO optimEstimator (pid=14445) Convergence is  achieved [ 0 ]
INFO optimEstimator (pid=14445) Log-Likelihood value =  -79.6629105970691
INFO spfrontier (pid=14445) Completed, status = 0
INFO spfrontier (pid=14445) Final estimates:
     Beta1      Beta2      Beta3     sigmaV     sigmaU 
4.12618894 9.76250494 1.79765749 0.01497415 2.30051601 
<-------------------------End pid= 14445
[1] "Mark =  92"
Start [pid= 14445  n = 50 , run = 2 ]-------------------------> -2.28343333422972
Log-likelihood (true DGP) (pid=14445) = -91.6509080951936
INFO spfrontier (pid=14445) Estimator started
INFO spfrontier (pid=14445) Calculating initial values
INFO calculateInitialValues (pid=14445) Initial values method errorsarlm
INFO spfrontier (pid=14445) Initial values:
     Beta1      Beta2      Beta3     sigmaV     sigmaU 
 1.5288196 10.6390541  1.2509212  0.8359614  2.0947003 
Maximizing -- use negfn and neggr
INFO doTryCatch (pid=14445) Calculating numeric hessian, number of required function calls is  122  ...
INFO optimEstimator (pid=14445) Convergence is  achieved [ 0 ]
INFO optimEstimator (pid=14445) Log-Likelihood value =  -89.0243943935539
INFO spfrontier (pid=14445) Completed, status = 0
INFO spfrontier (pid=14445) Final estimates:
     Beta1      Beta2      Beta3     sigmaV     sigmaU 
 3.8235779 10.5126460  1.1278940  0.5276554  2.3985818 
<-------------------------End pid= 14445
[1] "Mark =  48"
Start [pid= 14445  n = 100 , run = 1 ]-------------------------> -1.48369329781614
Log-likelihood (true DGP) (pid=14445) = -176.176710777771
INFO spfrontier (pid=14445) Estimator started
INFO spfrontier (pid=14445) Calculating initial values
INFO calculateInitialValues (pid=14445) Initial values method errorsarlm
INFO spfrontier (pid=14445) Initial values:
    Beta1     Beta2     Beta3    sigmaV    sigmaU 
3.3819685 9.9766585 0.7863375 0.5846982 2.3548633 
Maximizing -- use negfn and neggr
INFO doTryCatch (pid=14445) Calculating numeric hessian, number of required function calls is  122  ...
INFO optimEstimator (pid=14445) Convergence is  achieved [ 0 ]
INFO optimEstimator (pid=14445) Log-Likelihood value =  -175.449286035879
INFO spfrontier (pid=14445) Completed, status = 0
INFO spfrontier (pid=14445) Final estimates:
    Beta1     Beta2     Beta3    sigmaV    sigmaU 
5.2352487 9.8724719 0.9337123 0.4388289 2.4202759 
<-------------------------End pid= 14445
[1] "Mark =  35"
Start [pid= 14445  n = 100 , run = 2 ]-------------------------> 1.09278181091677
Log-likelihood (true DGP) (pid=14445) = -170.783923945398
INFO spfrontier (pid=14445) Estimator started
INFO spfrontier (pid=14445) Calculating initial values
INFO calculateInitialValues (pid=14445) Initial values method errorsarlm
INFO spfrontier (pid=14445) Initial values:
    Beta1     Beta2     Beta3    sigmaV    sigmaU 
3.1537994 9.9032982 1.2046796 0.3065791 2.2342946 
Maximizing -- use negfn and neggr
INFO doTryCatch (pid=14445) Calculating numeric hessian, number of required function calls is  122  ...
INFO optimEstimator (pid=14445) Convergence is  achieved [ 0 ]
INFO optimEstimator (pid=14445) Log-Likelihood value =  -168.399327906323
INFO spfrontier (pid=14445) Completed, status = 0
INFO spfrontier (pid=14445) Final estimates:
    Beta1     Beta2     Beta3    sigmaV    sigmaU 
4.9751404 9.9602544 1.0262273 0.5830375 2.0664144 
<-------------------------End pid= 14445
Executed in 3.24929308891296
   estimator   n beta0 beta1 beta2 sigmaV sigmaU    Mean   TV    Bias     SD
1    beta[0]  50     5    10     1    0.5    2.5  3.9749  5.0 -1.0251 0.2140
2    beta[0] 100     5    10     1    0.5    2.5  5.1052  5.0  0.1052 0.1839
3    beta[1]  50     5    10     1    0.5    2.5 10.1376 10.0  0.1376 0.5304
4    beta[1] 100     5    10     1    0.5    2.5  9.9164 10.0 -0.0836 0.0621
5    beta[2]  50     5    10     1    0.5    2.5  1.4628  1.0  0.4628 0.4736
6    beta[2] 100     5    10     1    0.5    2.5  0.9800  1.0 -0.0200 0.0654
7   sigma[v]  50     5    10     1    0.5    2.5  0.2713  0.5 -0.2287 0.3625
8   sigma[v] 100     5    10     1    0.5    2.5  0.5109  0.5  0.0109 0.1020
9   sigma[u]  50     5    10     1    0.5    2.5  2.3495  2.5 -0.1505 0.0693
10  sigma[u] 100     5    10     1    0.5    2.5  2.2433  2.5 -0.2567 0.2502
     rmse BiasPercentage
1  1.0472        -0.2050
2  0.2119         0.0210
3  0.5480         0.0138
4  0.1042        -0.0084
5  0.6622         0.4628
6  0.0684        -0.0200
7  0.4286        -0.4574
8  0.1026         0.0219
9  0.1657        -0.0602
10 0.3584        -0.1027