Compute posterior estimates of MIDAS lag weights

Computes posterior estimates of the normalized MIDAS lag weights from a fitted MIDAS model returned by fit_Minla_spatial(). The function draws samples from the posterior marginal distributions of the MIDAS hyperparameters and uses these samples to obtain the corresponding lag-weight functions.

Usage

compute_weights(model, n.samples = 200)

Arguments

model

A fitted MIDAS model returned by fit_Minla_spatial().

n.samples

Positive integer specifying the number of posterior samples drawn from the MIDAS hyperparameter marginal distributions to estimate the lag-weight distribution. Defaults to 200.

Value

A list containing one data frame for each high-frequency covariate in model$hf_input. The elements are named "hf_1", "hf_2", and so on. Each data frame contains:

lag

The MIDAS lag index.

mean

The posterior mean of the normalized lag weight.

q2.5

The 2.5% posterior quantile of the lag weight.

q97.5

The 97.5% posterior quantile of the lag weight.

Details

The supported MIDAS lag constraints are "hyperbolic", "gaussian", "beta1", "beta2", and "almon2". For each high-frequency covariate, the resulting weights are normalized to sum to one across all included lags.

Examples

if (FALSE) { # \dontrun{
if (requireNamespace("INLA", quietly = TRUE)) {
  data(data_spatialpoisson_example)

  g <- INLA::inla.read.graph(
    filename = system.file("map.adj", package = "midasINLA")
  )

  Midas_x1 <- prepare_Minla_spatial(
    x = data_spatialpoisson_example$data_x1$x1,
    loc_x = data_spatialpoisson_example$data_x1$loc,
    constraint = "hyperbolic",
    K = 0:29,
    m = 30,
    svc = TRUE,
    svc_prior = "icar",
    g = g
  )

  fit <- fit_Minla_spatial(
    formula = y ~ 1,
    data = data_spatialpoisson_example$data_y,
    loc_var = "loc",
    time_var = "Time",
    family = "poisson",
    hf_input = list(Midas_x1),
    inla_options = list(
      verbose = FALSE,
      control.predictor = list(
        compute = TRUE,
        link = 1
      )
    )
  )

  weights <- compute_weights(
    model = fit,
    n.samples = 200
  )

  head(weights$hf_1)
}
} # }