moving.estimation {VBV}

moving.estimation – estimate locally optimized trend and season figures

Description

moving.estimation – estimate locally optimized trend and season figures

Usage

moving.estimation(t.vec, y.vec, p, q.vec, m, base.period, lambda1, lambda2)

Arguments

t.vec

vector of points in time as integers

y.vec

vector of data

p

maximum exponent in polynomial for trend

q.vec

vector containing frequencies to use for seasonal component, given as integers, i.e. c(1, 3, 5) for 1/2pi, 3/2pi, 5/2*pi (times length of base period)

m

width of moving window

base.period

base period in number of observations, i.e. 12 for monthly data with yearly oscillations

lambda1

penalty weight for smoothness of trend

lambda2

penalty weight for smoothness of seasonal component

Value

A dataframe with the following components:

• dataoriginal data y.vec

• trendvector of estimated trend of length length(y.vec)

• seasonvector of estimated season of length length(y.vec)

Note

lambda1 == lambda2 == Inf result in estimations of the original Berliner Verfahren