Create a Gauss-Markov (GM) Process
Sets up the necessary backend for the GM process.
GM(beta = NULL, sigma2_gm = 1)
beta |
A |
sigma2_gm |
A |
When supplying values for beta and sigma^2[gm], these parameters should be of a GM process and NOT of an AR1. That is, do not supply AR1 parameters such as phi, sigma^2.
Internally, GM parameters are converted to AR1 using the 'freq' supplied when creating data objects (gts) or specifying a 'freq' parameter in simts or simts.imu.
The 'freq' of a data object takes precedence over the 'freq' set when modeling.
An S3 object with called ts.model with the following structure:
Used in summary: "BETA","SIGMA2"
beta, sigma^2[gm]
Number of parameters
String containing simplified model
"GM"
Depth of parameters e.g. list(1,1)
Guess starting values? TRUE or FALSE (e.g. specified value)
We consider the following model:
X_t = e^{(-β)} X_{t-1} + \varepsilon_t
, where \varepsilon_t is iid from a zero mean normal distribution with variance σ^2(1-e^{2β}).
James Balamuta
GM() GM(beta=.32, sigma2_gm=1.3)
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