amidas_table: Weight and lag selection table for aggregates based MIDAS...

In mpiktas/midasr: Mixed Data Sampling Regression

Weight and lag selection table for aggregates based MIDAS regression model

Description

Create weight and lag selection table for the aggregates based MIDAS regression model

Usage

amidas_table(
  formula,
  data,
  weights,
  wstart,
  type,
  start = NULL,
  from,
  to,
  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
weights	the names of weights used in Ghysels schema
wstart	the starting values for the weights of the firs low frequency lag
type	the type of Ghysels schema see amweights, can be a vector of types
start	the starting values for optimisation excluding the starting values for the last term
from	a named list, or named vector with high frequency (NB!) lag numbers which are the beginnings of MIDAS lag structures. The names should correspond to the MIDAS lag terms in the formula for which to do the lag selection. Value NA indicates lag start at zero
to	to a named list where each element is a vector with two elements. The first element is the low frequency lag number from which the lag selection starts, the second is the low frequency lag number at which the lag selection ends. NA indicates lowest (highest) lag numbers possible.
IC	the names of information criteria which should be calculated
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 sequentialy increasing the midas lag from kmin to kmax and varying the weights of the last term of the given formula

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

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
test	the argument test
weights	the names of weight functions
lags	the lags used in models

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)

tb <- amidas_table(y~trend+fmls(x,12,12,nealmon),
                   data=list(y=y,x=x,trend=trend),
                   weights=c("nealmon"),wstart=list(nealmon=c(0,0,0)),
                   start=list(trend=1),type=c("A"),
                   from=0,to=c(1,2))

mpiktas/midasr documentation built on Aug. 24, 2022, 2:32 p.m.