weights_table: Create a weight function selection table for MIDAS regression...

In midasr: Mixed Data Sampling Regression

Description

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

Usage

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

Details

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

Value

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

Author(s)

Virmantas Kvedaras, Vaidotas Zemlys

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

midasr documentation built on Feb. 23, 2021, 5:11 p.m.