EstEval: Evaluate an estimation method

Description

Evaluate an estimation method.

Usage

EstEval(model, replications=100, rng=NULL, quiet=FALSE, 
                       simulation.args=NULL,
                       estimation=NULL, estimation.args=NULL, 
                       criterion ="coef", criterion.args =NULL) 

    is.EstEval(obj)

Value

A list with element result of length replications, each element containing the results of criterion(estimation(simulate(model))). Other elements of the list contain information from the supplied arguments.

Arguments

model: A TSmodel.
replications: The number of simulations.
rng: The RNG and starting seed.
quiet: If TRUE then no information is printed during estimation.
simulation.args: A list of any arguments to pass to simulate.
estimation: A character string indicating the estimation routine to use.
estimation.args: A list of any arguments to pass to the estimation routine.
criterion: A function to apply to the results of estimation to extract the information which is to be retained.
criterion.args: A list of any arguments to be passed to the criterion function.

obj: an object.

Details

estimation.args and criterion.args should be NULL if no args are needed. If model is an object of class 'EstEval' or 'simulation' then the model and the seed!!! are extracted so the evaluation will be based on the same generated sample. criterion can be 'coef', 'roots', 'TSmodel', or 'TSestModel'. With the default (coef) or with TSmodel the other criteria can be reconstructed (when the estimation method finds a known form for the model - which is not always the case, for example with estBlackBox methods). If criterion = 'roots' then criterion.args= list(verbose=FALSE) is advised.

Examples

Run this code

    data("eg1.DSE.data.diff", package="dse")
    model <- estVARXls(TSdata(output=outputData(eg1.DSE.data.diff)))
    z <-  EstEval(model, 
       estimation="estVARXls", estimation.args=list(max.lag=2))
    tfplot(z)
    zz <-  EstEval(model, 
       estimation="estVARXls", estimation.args=list(max.lag=2),
       simulation.args=list(sampleT=50, sd=1.5))
    is.EstEval(z)