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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, ...)
the formula for MIDAS regression, the lag selection is performed for the last MIDAS lag term in the formula
a list containing data with mixed frequencies
the names of weights used in Ghysels schema
the starting values for the weights of the firs low frequency lag
the type of Ghysels schema see amweights, can be a vector of types
the starting values for optimisation excluding the starting values for the last term
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 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.
the names of information criteria which should be calculated
the names of statistical tests to perform on restricted model, p-values are reported in the columns of model selection table
see midasr
see midas_r
additional parameters to optimisation function, see midas_r
a midas_r_ic_table
object which is the list with the following elements:
the table where each row contains calculated information criteria for both restricted and unrestricted MIDAS regression model with given lag structure
the list containing fitted models
the argument IC
the argument test
the names of weight functions
the lags used in models
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
# NOT RUN {
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))
# }
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