est.max.like {dse2}R Documentation

Maximum Likelihood Estimation

Description

Maximum likelihood estimation.

Usage

    est.max.like(obj, ...) 
    est.max.like(obj, data, algorithm="optim",
        algorithm.args=list(method="BFGS", upper=Inf, lower=-Inf, hessian=TRUE))
    est.max.like(obj, data=TSdata(obj), ...)
    est.max.like(obj, model, ...) 

Arguments

obj An object of class TSmodel, TSdata or TSestModel
data A TSdata object.
model A TSmodel object.
algorithm The algorithm ('optim', 'nlm' or 'nlmin') to use for maximization.
algorithm.args Arguments for the optimization algorithm.

Details

Shape is used to specify both the initial parameter values and the model structure (the placement of the parameters in the various arrays of the TSmodel). Estimation attempts to minimimize the negative log likelihood (as returned by l ) of the given model structure by adjusting the parameter values. A TSmodel can also have constant values in some array elements, and these are not changed.

Value

The value returned is an object of class TSestModel with additional elements est$converged, which is T or F indicating convergence, and est$results. The hessian and gradient in results could potentially be used for restarting in the case of non-convergence, but that has not yet been implemented.

See Also

optim nlm est.VARX.ls bft TSmodel l

Examples

  true.model <- ARMA(A=c(1, 0.5), B=1)
  est.model <-  est.max.like(true.model,  simulate(true.model))
  summary(est.model)
  est.model
  tfplot(est.model)