MARSS (version 3.2)

MARSSinnovationsboot: Bootstrapped Data using Stoffer and Wall's Algorithm

Description

Creates bootstrap data via sampling from the standardized innovations matrix. This is a base function in the MARSS-package.

Usage

MARSSinnovationsboot(MLEobj, nboot = 1000, minIndx = 3)

Arguments

MLEobj

An object of class marssMLE. This object must have a $par element containing MLE parameter estimates from e.g. MARSSkem or

nboot

Number of bootstraps to perform.

minIndx

Number of innovations to skip. Stoffer & Wall suggest not sampling from innovations 1-3.

Value

• A list containing the following components:
• boot.states
  Array (dim is m x tSteps x nboot) of simulated state processes.
• boot.data
  Array (dim is n x tSteps x nboot) of simulated data.
• model
  MARSS model ($model element of the marssMLE object.
• nboot
  Number of bootstraps performed.
• m is the number state processes (x in the MARSS model) and n is the number of observation time series (y in the MARSS model).

Details

Stoffer and Wall (1991) present an algorithm for generating CIs via a non-parametric bootstrap for state-space models. The basic idea is that the Kalman filter can be used to generate estimates of the residuals of the model fit. These residuals are then standardized and resampled and used to generate bootstrapped data using the MARSS model and its maximum-likelihood parameter estimates. One of the limitations of the Stoffer and Wall algorithm is that it cannot be used when there are missing data, unless all data at time t are missing.

References

Stoffer, D. S., and K. D. Wall. 1991. Bootstrapping state-space models: Gaussian maximum likelihood estimation and the Kalman filter. Journal of the American Statistical Association 86:1024-1033.

See Also

stdInnov MARSSparamCIs MARSSboot

Examples

dat = t(harborSealnomiss)
  dat = dat[2:3,]
  MLEobj = MARSS(dat, model=list(U="equal",Q=diag(.01,2)))
  boot.obj = MARSSinnovationsboot(MLEobj)