fitSSM: Maximum Likelihood Estimation of a State Space Model

Description

Function fitSSM finds the maximum likelihood estimates for unknown parameters of an arbitary state space model if an user defined model building function is defined. As a default, fitSSM estimates the non-zero elements, which are marked as NA, of the time-invariant covariance matrices H and Q of the given model.

Usage

fitSSM(inits, model = NULL, modFun = NULL,
    method = "BFGS", nsim = 0, antithetics = TRUE,
    taylor = TRUE, theta = NULL, maxiter = 500, ...)

Arguments

inits

Initial values for optim

model

Model object of class SSModel. if ModFun is defined, this argument is ignored.

modFun

User defined function which builds the model of class SSModel given the parameters. If NULL, default estimation procedure is used (See details).

method

The method to be used in optim. Default is "BFGS".

nsim

Number of independent samples used in estimating the log-likelihood of the non-gaussian state space object. Default is 0, which gives good starting value for optimisation. Only used in case of non-Gaussian state space model.

antithetics

Logical. If TRUE, two antithetic variables are used in simulations, one for location and another for scale. Default is TRUE. Only used in case of non-Gaussian state space model.

taylor

Logical. If TRUE, control variable based on Taylor approximation is used. Default is TRUE. Only used in case of non-Gaussian state space model.

theta

Initial values for conditional mode theta. Default is object$y. Only used in case of non-Gaussian state space model.

maxiter

Maximum number of iterations used in linearisation. Only used in case of non-Gaussian state space model.

...

Optional arguments for functions optim and modFun.

Value

A list with elements
optim.outOutput from function optim.
modelModel with estimated parameters.