add.shared.local.level: Local level trend state component
In michelletran/bsts: Bayesian Structural Time Series

Description Usage Arguments Value Author(s) References See Also

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 ipmly that the observation coefficients B form a rectangular lower triangular matrix with diagonal 1.0.

  AddSharedLocalLevel(
     state.specification,
     response,
     nfactors, 
     coefficient.prior = NULL,
     initial.state.prior = NULL,
     timestamps = NULL,
     series.id = NULL,
     sdy,
     ...)

`state.specification`	A pre-existing 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 `timestamps` and `series.id` are required. Otherwise they are unused.
`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. This currently must be a `ScaledMatrixNormalPrior`. Expect other choices in the future.
`initial.state.prior`	An object of class `MvnPrior`, describing the prior distribution of the initial state vector (at time 1).
`timestamps`	If `response` is in long format (i.e. a vector instead of a matrix) this argument is a vector of the same length indicating the time index to which each element of `response` belongs.
`series.id`	If `response` is in long format (i.e. a vector instead of a matrix) this argument is a vector of the same length indicating the time series to which each element of `response` belongs.
`sdy`	A vector giving the standard deviation of each series to be modeled. This argument is only necessary if `response` cannot be supplied directly.
`...`	Extra arguments passed to `ConditionalZellnerPrior`, used to create a default prior for the observation coefficients when `coefficient.prior` is left as `NULL`.