weights_table function

Create a weight function selection table for MIDAS regression model

Creates a weight function selection table for MIDAS regression model with given information criteria and weight functions.

weights_table(
  formula,
  data,
  start = NULL,
  IC = c("AIC", "BIC"),
  test = c("hAh_test"),
  Ofunction = "optim",
  weight_gradients = NULL,
  ...
)

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
• IC: the 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
• ...: 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 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)
mwr <- weights_table(y~trend+fmls(x,12,12,nealmon),
                     start=list(x=list(nealmon=rep(0,3),
                     nbeta=c(1,1,1,0))))

mwr

Author(s)

Virmantas Kvedaras, Vaidotas Zemlys