midas_r_ic_table function

Create a weight and lag selection table for MIDAS regression model

Creates a weight and lag selection table for MIDAS regression model with given information criteria and minimum and maximum lags.

midas_r_ic_table(
  formula,
  data = NULL,
  start = NULL,
  table,
  IC = c("AIC", "BIC"),
  test = c("hAh_test"),
  Ofunction = "optim",
  weight_gradients = NULL,
  show_progress = TRUE,
  ...
)

Arguments

• formula: the formula for MIDAS regression, the lag selection is performed for the last MIDAS lag term in the formula
• data: a list containing data with mixed frequencies
• start: the starting values for optimisation excluding the starting values for the last term
• table: an wls_table object, see expand_weights_lags
• IC: the names of information criteria which to compute
• test: the names of statistical tests to perform on restricted model, p-values are reported in the columns of model selection table
• Ofunction: see midasr
• weight_gradients: see midas_r
• show_progress: logical, TRUE to show progress bar, FALSE for silent evaluation
• ...: additional parameters to optimisation function, see midas_r

Returns

a midas_r_ic_table object which is the list with the following elements:

• table: the table where each row contains calculated information criteria for both restricted and unrestricted MIDAS regression model with given lag structure

• candlist: the list containing fitted models

• IC: the argument IC

Details

This function estimates models sequentially increasing the midas lag from kmin to kmax and varying the weights of the last term of the given formula

Examples

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

 
mwlr <- midas_r_ic_table(y~trend+fmls(x,12,12,nealmon),
                   table=list(x=list(weights=
                   as.list(c("nealmon","nealmon","nbeta")),
                   lags=list(0:4,0:5,0:6),
                   starts=list(rep(0,3),rep(0,3,),c(1,1,1,0)))))

mwlr

Author(s)

Virmantas Kvedaras, Vaidotas Zemlys