gmwm_engine: Engine for obtaining the GMWM Estimator
In SMAC-Group/gmwm: Generalized Method of Wavelet Moments

Description Usage Arguments Details Value Author(s) References

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

1 2	gmwm_engine(theta, desc, objdesc, model_type, wv_empir, omega, scales, starting)

`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).

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.

A vec that contains the parameter estimates from GMWM estimator.

JJB

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/gmwm documentation built on Sept. 11, 2021, 10:06 a.m.