amweights: Weights for aggregates based MIDAS regressions

In midasr: Mixed Data Sampling Regression

Description

Produces weights for aggregates based MIDAS regression

Usage

amweights(p, d, m, weight = nealmon, type = c("A", "B", "C"))

Arguments

p	parameters for weight functions, see details.
d	number of high frequency lags
m	the frequency
weight	the weight function
type	type of structure, a string, one of A, B or C.

Details

Suppose a weight function w(β,θ) satisfies the following equation:

w(β,θ)=β g(θ)

The following combinations are defined, corresponding to structure types A, B and C respectively:

(w(β_1,θ_1),...,w(β_k,θ_k))

(w(β_1,θ),...,w(β_k,θ))

β(w(1,θ),...,w(1,θ)),

where k is the number of low frequency lags, i.e. d/m. If the latter value is not whole number, the error is produced.

The starting values p should be supplied then as follows:

(β_1,θ_1,...,β_k,θ_k)

(β_1,...,β_k,θ)

(β,θ)

Value

a vector of weights

Author(s)

Virmantas Kvedaras, Vaidotas Zemlys

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