bflSmooth

bflSmooth smoothes a time series into a time series of a higher frequency that
exactly aggregates into the higher one. The process followed is Boot, Feibes
and Lisman, which minimizes the squares of the variations.

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 

bflSmooth function

<dl><dt>lfserie</dt>
<dd>a time series to be smoothed</dd>
<dt>nfrequency</dt>
<dd>the new high frequency. It must be a multiple of the low
frequency.</dd>
<dt>weights</dt>
<dd>NULL or a time series of the same size than the expected
high-frequency serie.</dd>
<dt>lfserie.is.rate</dt>
<dd>TRUE or FALSE. Only taken into account if weights isn't
NULL.</dd></dl>

Arguments

Smooth a time series — bflSmooth

<dl>

<dt>lfserie</dt>
<dd>a time series to be smoothed</dd>


<dt>nfrequency</dt>
<dd>the new high frequency. It must be a multiple of the low
frequency.</dd>


<dt>weights</dt>
<dd>NULL or a time series of the same size than the expected
high-frequency serie.</dd>


<dt>lfserie.is.rate</dt>
<dd>TRUE or FALSE. Only taken into account if weights isn't
NULL.</dd>

</dl>

bflSmooth: Smooth a time series

Description

Usage

Value

Arguments

Details