simulate.midas_r function

Simulate MIDAS regression response

Simulates one or more responses from the distribution corresponding to a fitted MIDAS regression object.

## S3 method for class 'midas_r'
simulate(
  object,
  nsim = 999,
  seed = NULL,
  future = TRUE,
  newdata = NULL,
  insample = NULL,
  method = c("static", "dynamic"),
  innov = NULL,
  show_progress = TRUE,
  ...
)

Arguments

• object: midas_r object

• nsim: number of simulations

• seed: either NULL or an integer that will be used in a call to set.seed before simulating the time series. The default, NULL will not change the random generator state.

• future: logical, if TRUE forecasts are simulated, if FALSE in-sample simulation is performed.

• newdata: a named list containing future values of mixed frequency regressors. The default is NULL, meaning that only in-sample data is used.

• insample: a list containing the historic mixed frequency data

• method: the simulation method, if "static" in-sample values for dependent variable are used in autoregressive MIDAS model, if "dynamic"

  the dependent variable values are calculated step-by-step from the initial in-sample values.

• innov: a matrix containing the simulated innovations. The default is NULL, meaning, that innovations are simulated from model residuals.

• show_progress: logical, TRUE to show progress bar, FALSE for silent evaluation

• ...: not used currently

Returns

a matrix of simulated responses. Each row contains a simulated response.

Details

Only the regression innovations are simulated, it is assumed that the predictor variables and coefficients are fixed. The innovation distribution is simulated via bootstrap.

Examples

data("USrealgdp")
data("USunempr")

y <- diff(log(USrealgdp))
x <- window(diff(USunempr), start = 1949)
trend <- 1:length(y)

##24 high frequency lags of x included
mr <- midas_r(y ~ trend + fmls(x, 23, 12, nealmon), start = list(x = rep(0, 3)))

simulate(mr, nsim=10, future=FALSE)

##Forecast horizon
h <- 3
##Declining unemployment
xn <- rep(-0.1, 12*3)
##New trend values
trendn <- length(y) + 1:h

simulate(mr, nsim = 10, future = TRUE, newdata = list(trend = trendn, x = xn))

Author(s)

Virmantas Kvedaras, Vaidotas Zemlys