amidas_table function

Weight and lag selection table for aggregates based MIDAS regression model

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

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

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

• test: the argument test

• weights: the names of weight functions

• lags: the lags used in models

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

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))

Author(s)

Virmantas Kvedaras, Vaidotas Zemlys