gmwm_engine: Engine for obtaining the GMWM Estimator

In SMAC-Group/simts: Time Series Analysis Tools

Engine for obtaining the GMWM Estimator

Description

This function uses the Generalized Method of Wavelet Moments (GMWM) to estimate the parameters of a time series model.

Usage

gmwm_engine(
  theta,
  desc,
  objdesc,
  model_type,
  wv_empir,
  omega,
  scales,
  starting
)

Arguments

theta	A vec with dimensions N x 1 that contains user-supplied initial values for parameters
desc	A vector<string> indicating the models that should be considered.
objdesc	A field<vec> containing a list of parameters (e.g. AR(1) = c(1,1), ARMA(p,q) = c(p,q,1))
model_type	A string that represents the model transformation
wv_empir	A vec that contains the empirical wavelet variance
omega	A mat that represents the covariance matrix.
scales	A vec that contains the scales or taus (2^(1:J))
starting	A bool that indicates whether we guessed starting (T) or the user supplied estimates (F).

Details

If type = "imu" or "ssm", then parameter vector should indicate the characters of the models that compose the latent or state-space model. The model options are:

• "AR1"a first order autoregressive process with parameters (\phi,\sigma^2)

• "ARMA"an autoregressive moving average process with parameters (\phi _p, \theta _q, \sigma^2)

• "DR"a drift with parameter \omega

• "QN"a quantization noise process with parameter Q

• "RW"a random walk process with parameter \sigma^2

• "WN"a white noise process with parameter \sigma^2

If model_type = "imu" or type = "ssm" then starting values pass through an initial bootstrap and pseudo-optimization before being passed to the GMWM optimization. If robust = TRUE the function takes the robust estimate of the wavelet variance to be used in the GMWM estimation procedure.

Value

A vec that contains the parameter estimates from GMWM estimator.

Author(s)

JJB

References

Wavelet variance based estimation for composite stochastic processes, S. Guerrier and Robust Inference for Time Series Models: a Wavelet-Based Framework, S. Guerrier

SMAC-Group/simts documentation built on Sept. 4, 2023, 5:25 a.m.