moving.estimation

moving.estimation -- estimate locally optimized trend and season figures

Time series decomposition for univariate time series using the
"Verallgemeinerte Berliner Verfahren" (Generalized Berlin Method) as
described in 'Kontinuierliche Messgrößen und Stichprobenstrategien in
Raum und Zeit mit Anwendungen in den Natur-, Umwelt-, Wirtschafts-
und Finanzwissenschaften', by
Hebbel and Steuer, Springer Berlin Heidelberg, 2022
<doi:10.1007/978-3-662-65638-9>, or
'Decomposition of Time Series using the Generalised Berlin
Method (VBV)' by Hebbel and Steuer, in Jan Beran, Yuanhua Feng, Hartmut
Hebbel (Eds.): Empirical Economic and Financial Research - Theory,
Methods and Practice, Festschrift in Honour of Prof. Siegfried Heiler.
Series: Advanced Studies in Theoretical and Applied Econometrics.
Springer 2014, p. 9-40.

Detlef Steuer

The Generalized Berlin Method for Time Series Decomposition

Hartmut Hebbel

moving.estimation function

<dl><dt>t.vec</dt>
<dd>vector of points in time as integers</dd>
<dt>y.vec</dt>
<dd>vector of data</dd>
<dt>p</dt>
<dd>maximum exponent in polynomial for trend</dd>
<dt>q.vec</dt>
<dd>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)</dd>
<dt>m</dt>
<dd>width of moving window</dd>
<dt>base.period</dt>
<dd>base period in number of observations, i.e. 12 for monthly data with yearly oscillations</dd>
<dt>lambda1</dt>
<dd>penalty weight for smoothness of trend</dd>
<dt>lambda2</dt>
<dd>penalty weight for smoothness of seasonal component</dd></dl>

Arguments

moving.estimation -- estimate locally optimized trend and season figures — moving.estimation

<dl>

<dt>t.vec</dt>
<dd>vector of points in time as integers</dd>


<dt>y.vec</dt>
<dd>vector of data</dd>


<dt>p</dt>
<dd>maximum exponent in polynomial for trend</dd>


<dt>q.vec</dt>
<dd>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)</dd>


<dt>m</dt>
<dd>width of moving window</dd>


<dt>base.period</dt>
<dd>base period in number of observations, i.e. 12 for monthly data with yearly oscillations</dd>


<dt>lambda1</dt>
<dd>penalty weight for smoothness of trend</dd>


<dt>lambda2</dt>
<dd>penalty weight for smoothness of seasonal component</dd>

</dl>

moving.estimation: moving.estimation -- estimate locally optimized trend and season figures

Description

Usage

Value

Arguments