noise_block() R function from [kDGLM]

noise_block

Creates the structure for a Noise block. This block represents an independent random noise that should be added to the linear predictor. The variance of the noise cannot be formally estimated, as such we use a discount strategy similar to that of \insertCite WestHarr-DLM;textualkDGLM to specify it.


noise_block(..., name = "Noise", D = 0.99, R1 = 0.1, H = 0)

noise(name = "Noise", D = 0.99, R1 = 0.1, H = 0, X = 1)

Arguments

...: Named values for the planning matrix.
name: String: An optional argument providing the name for this block. Can be useful to identify the models with meaningful labels, also, the name used will be used in some auxiliary functions.
D: scalar or vector: A sequence of values specifying the desired discount factor for each time. It should have length 1 or t, where t is the size of the series. If both D and H are specified, the value of D is ignored.
R1: scalar: The prior variance of the noise.
H: scalar: The variance of the noise. If both D and H are specified, the value of D is ignored.
X: Vector or scalar: An argument providing the values of the covariate X_t.

Returns

A dlm_block object containing the following values:

FF Array: A 3D-array containing the regression matrix for each time. Its dimension should be n x k x t, where n is the number of latent states, k is the number of linear predictors in the model and t is the time series length.
FF.labs Matrix: A n x k character matrix describing the type of value of each element of FF.
G Matrix: A 3D-array containing the evolution matrix for each time. Its dimension should be n x n x t, where n is the number of latent states and t is the time series length.
G.labs Matrix: A n x n character matrix describing the type of value of each element of G.
D Array: A 3D-array containing the discount factor matrix for each time. Its dimension should be n x n x t, where n is the number of latent states and t is the time series length.
H Array: A 3D-array containing the covariance matrix of the noise for each time. Its dimension should be the same as D.
a1 Vector: The prior mean for the latent vector.
R1 Matrix: The prior covariance matrix for the latent vector.
var.names list: A list containing the variables indexes by their name.
order Positive integer: Same as argument.
n Positive integer: The number of latent states associated with this block (2).
t Positive integer: The number of time steps associated with this block. If 1, the block is compatible with blocks of any time length, but if t is greater than 1, this block can only be used with blocks of the same time length.
k Positive integer: The number of outcomes associated with this block. This block can only be used with blocks with the same outcome length.
pred.names Vector: The name of the linear predictors associated with this block.
monitoring Vector: The combination of monitoring, monitoring and monitoring.pulse.
type Character: The type of block (Noise).

Details

For the details about the implementation see \insertCite ArtigoPacote;textualkDGLM.

For the details about dynamic regression models in the context of DLMs, see \insertCite WestHarr-DLM;textualkDGLM, chapters 6 and 9.

Examples


noise_block(mu = 1, D = 0.99, R1 = 1e-2)

References

\insertAllCited

noise_block function