in_sample

The function <code>in_sample</code> returns in-sample predictions from a praislm
or a twoStepsBenchmark object.

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_sample function

<dl><dt>object</dt>
<dd>an object of class <code>"praislm"</code> or <code>"twoStepsBenchmark"</code>.</dd>
<dt>type</dt>
<dd><code>"changes"</code> or <code>"levels"</code>. The results are either returned
in changes or in levels.</dd></dl>

Arguments

Producing the in sample predictions of a prais-lm regression — in_sample

<dl>

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


<dt>type</dt>
<dd><code>"changes"</code> or <code>"levels"</code>. The results are either returned
in changes or in levels.</dd>

</dl>

in_sample: Producing the in sample predictions of a prais-lm regression

Description

Usage

Value

Arguments

Details

See Also

Examples