in_disaggr

The function <code>in_disaggr</code> takes a twoStepsBenchmark or a
threeRuleSmooth object as an input. It produces a comparison between
the benchmarked time series and the high-frequency input.

The twoStepsBenchmark() and threeRuleSmooth() functions allow you to
disaggregate a low-frequency time series with higher frequency time series,
using the French National Accounts methodology. The aggregated sum of the
resulting time series is strictly equal to the low-frequency time series within the
benchmarking window. Typically, the low-frequency time series is an annual one,
unknown for the last year, and the high frequency one is either quarterly or
monthly. See "Methodology of quarterly national accounts", Insee Méthodes
N°126, by Insee (2012, ISBN:978-2-11-068613-8, <https://www.insee.fr/en/information/2579410>).

Pauline Meinzel

disaggR

Two-Steps Benchmarks for Time Series Disaggregation

Arnaud Feldmann

Thomas Laurent

Franck Arnaud

Institut national de la statistique et des études économiques 

in_disaggr function

<dl><dt>object</dt>
<dd>an object of class <code>"twoStepsBenchmark"</code> or <code>"threeRuleSmooth"</code>.</dd>
<dt>type</dt>
<dd><code>"levels"</code>,<code>"levels-rebased"</code>, <code>"changes"</code> or <code>"contributions"</code>.
This defines the type of output.</dd></dl>

Arguments

Comparing a disaggregation with the high-frequency input — in_disaggr

<dl>

<dt>object</dt>
<dd>an object of class <code>"twoStepsBenchmark"</code> or <code>"threeRuleSmooth"</code>.</dd>


<dt>type</dt>
<dd><code>"levels"</code>,<code>"levels-rebased"</code>, <code>"changes"</code> or <code>"contributions"</code>.
This defines the type of output.</dd>

</dl>

in_disaggr: Comparing a disaggregation with the high-frequency input

Description

Usage

Value

Arguments

Details

See Also

Examples