bsts (version 0.6.1)

mixed.frequency: Models for mixed frequency time series

Description

Fit a structured time series to mixed frequncy data.

Usage

bsts.mixed(target.series,
              predictors,
              which.coarse.interval,
              membership.fraction,
              contains.end,
              state.specification,
              regression.prior,
              niter,
              ping = niter / 10,
              seed = NULL,
              truth = NULL,
              ...)

Arguments

target.series

A vector object of class zoo indexed by calendar dates. The date associated with each element is the LAST DAY in the time interval measured by the corresponding value. The value is what Harvey (

predictors

A matrix of class zoo indexed by calendar dates. The date associated with each row is the LAST DAY in the time interval encompasing the measurement. The dates are expected to be at a finer scal

which.coarse.interval

A numeric vector of length nrow(predictors) giving the index of the coarse interval corresponding to the end of each fine interval.

membership.fraction

A numeric vector of length nrow(predictors) giving the fraction of activity attributed to the coarse interval corresponding to the beginning of each fine interval. This is always positive, and will be 1 except when a fine int

contains.end

A logical vector of length nrow(predictors) indicating whether each fine interval contains the end of a coarse interval.

state.specification

A state specification like that required by bsts.

regression.prior

A prior distribution created by SpikeSlabPrior. A default prior will be generated if none is specified.

niter

The desired number of MCMC iterations.

ping

An integer indicating the frequency with which progress reports get printed. E.g. setting ping = 100 will print a status message with a time and iteration stamp every 100 iterations. If you don't want these messages set pin

seed

An integer to use as the random seed for the underlying C++ code. If NULL then the seed will be set using the clock.

truth

For debugging purposes only. A list containing one or more of the following elements. If any are present then corresponding values will be held fixed in the MCMC algorithm.

• A matrix namedstatecontaining the state of the

...

Extra arguments passed to SpikeSlabPrior

Value

• An object of class bsts.mixed, which is a list with the following elements. Many of these are arrays, in which case the first index of the array corresponds to the MCMC iteration number.
• coefficients
  A matrix containing the MCMC draws of the regression coefficients. Rows correspond to MCMC draws, and columns correspond to variables.
• sigma.obs
  The standard deviation of the weekly latent observations.
• state.contributions
  A three-dimensional array containing the MCMC draws of each state model's contributions to the state of the weekly model. The three dimensions are MCMC iteration, state model, and week number.
• weekly
  A matrix of MCMC draws of the weekly latent observations. Rows are MCMC iterations, and columns are weekly time points.
• cumulator
  A matrix of MCMC draws of the cumulator variable.
• The returned object also contains MCMC draws for the parameters of the state models supplied as part of state.specification, relevant information passed to the function call, and other supplemental information.

References

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.

See Also

bsts, AddLocalLevel, AddLocalLinearTrend, AddGeneralizedLocalLinearTrend, SpikeSlabPrior, SdPrior.

Examples

data <- SimulateFakeMixedFrequencyData(nweeks = 104, xdim = 20)

  ## Setting an upper limit on the standard deviations can help keep the
  ## MCMC from flying off to infinity.
  sd.limit <- sd(data$coarse.target)
  state.specification <-
       AddLocalLinearTrend(list(),
                     data$coarse.target,
                     level.sigma.prior = SdPrior(1.0, 5, upper.limit = sd.limit),
                     slope.sigma.prior = SdPrior(.5, 5, upper.limit = sd.limit))
  weeks <- index(data$predictor)
  months <- index(data$coarse.target)
  which.month <- MatchWeekToMonth(weeks, months[1])
  membership.fraction <- GetFractionOfDaysInInitialMonth(weeks)
  contains.end <- WeekEndsMonth(weeks)

  model <- bsts.mixed(target.series = data$coarse.target,
                      predictors = data$predictors,
                      membership.fraction = membership.fraction,
                      contains.end = contains.end,
                      which.coarse = which.month,
                      state.specification = state.specification,
                      niter = 500,
                      expected.r2 = .999,
                      prior.df = 1)

  plot(model, "state")
  plot(model, "components")