add.shared.local.level  R Documentation 
Add a shared local level model to a state specification. The shared local level model assumes the trend is a multivariate random walk:
alpha[t+1, ] = alpha[t, ] + rnorm(nfactors, 0, sigma).
The contribution to the mean of the observed series obeys
y[t, ] = B %*% alpha[t, ]
plus
observation error. Identifiability constraints imply that the
observation coefficients B
form a rectangular lower triangular
matrix with diagonal 1.0.
If there are m time series and p factors, then B has m rows and p columns. Having B be lower triangular means that the first factor affects all series. The second affects all but the first, the third excludes the first two, etc.
AddSharedLocalLevel( state.specification, response, nfactors, coefficient.prior = NULL, initial.state.prior = NULL, timestamps = NULL, series.id = NULL, sdy, ...)
state.specification 
A preexisting list of state components that you wish to add to. If omitted, an empty list will be assumed. 
response 
The time series to be modeled. This can either be a
matrix with rows as time and columns as series, or it can be a numeric
vector. If a vector is passed then 
nfactors 
The number of latent factors to include in the model. This is the dimension of the state for this model component. 
coefficient.prior 
Prior distribution on the observation coefficients. 
initial.state.prior 
An object of class

timestamps 
If 
series.id 
If 
sdy 
A vector giving the standard deviation of each series to be
modeled. This argument is only necessary if 
... 
Extra arguments passed to

Returns a list with the elements necessary to specify a local linear trend state model.
Steven L. Scott steve.the.bayesian@gmail.com
Harvey (1990), "Forecasting, structural time series, and the Kalman filter", Cambridge University Press.
Durbin and Koopman (2001), "Time series analysis by state space methods", Oxford University Press.
bsts
.
SdPrior
NormalPrior
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