outliers

The function <code>outliers</code> returns the outliers
from either a praislm or a twoStepsBenchmark object.

internal

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 

outliers function

<dl><dt>object</dt>
<dd>a praislm or twoStepsBenchmark object.</dd>
<dt>as.ts</dt>
<dd>a boolean of length 1. If <code>TRUE</code>, the returned
outliers are returned as a time series with (dim and colnames).
If <code>FALSE</code>, the returned outliers is the named list that was
submitted as a function argument.</dd></dl>

Arguments

Extracting the standard error — outliers

<dl>

<dt>object</dt>
<dd>a praislm or twoStepsBenchmark object.</dd>


<dt>as.ts</dt>
<dd>a boolean of length 1. If <code>TRUE</code>, the returned
outliers are returned as a time series with (dim and colnames).
If <code>FALSE</code>, the returned outliers is the named list that was
submitted as a function argument.</dd>

</dl>

outliers: Extracting the standard error

Description

Usage

Value

Arguments