mensalize_sidra_series: Convert Rolling Quarters to Exact Monthly Series

In PNADCperiods: Identify Reference Periods in Brazil's PNADC Survey Data

Convert Rolling Quarters to Exact Monthly Series

Description

Transforms SIDRA rolling quarterly averages into exact monthly values using the mathematical relationship between consecutive rolling quarters.

Usage

mensalize_sidra_series(
  rolling_quarters,
  starting_points = NULL,
  series = "all",
  compute_derived = TRUE,
  verbose = TRUE
)

Arguments

rolling_quarters	data.table from fetch_sidra_rolling_quarters containing rolling quarter series with columns anomesfinaltrimmovel, mesnotrim, and series value columns.
starting_points	Optional data.table with precomputed starting points (y0 values). If NULL (default), uses bundled pnadc_series_starting_points. See Details for format.
series	Character vector of series names to mensalize, or "all" (default) for all series in the input data (except price indices).
compute_derived	Logical. Compute derived series (rates, aggregates)? Default TRUE.
verbose	Logical. Print progress messages? Default TRUE.

Details

The algorithm exploits the mathematical property of rolling quarterly averages:

RQ_t - RQ_{t-1} = (Month_t - Month_{t-3}) / 3

This means exact 3-month variations can be extracted from consecutive rolling quarters. By accumulating these variations separately for each month-position (1, 2, or 3), we build cumulative variation series. The only unknown is the starting level for Jan, Feb, and Mar 2012.

Starting points are estimated by:

1. Computing monthly estimates from calibrated microdata (z_ variables)

2. Calculating cumulative variations from SIDRA (cum_ variables)

3. Backprojecting: e0 = z_ - cum_ over calibration period (2013-2019)

4. Averaging e0 by month position to get y0_ for each position

Final adjustment ensures the average of 3 consecutive mensalized values equals the original rolling quarter value.

Value

A data.table with columns:

anomesexato

Integer. YYYYMM exact month

m_*

Numeric. Mensalized value for each series (one column per series)

Starting Points Format

If providing custom starting points, the data.table must have columns:

• series_name: Character. Series name matching rolling_quarters columns

• mesnotrim: Integer (1, 2, or 3). Month position in quarter

• y0: Numeric. Starting point value

Mathematical Foundation

The mensalization algorithm proceeds in steps:

1. Calculate d3 = 3 * (RQ_t - RQ_t-1)

2. Separate d3 by month position: d3m1, d3m2, d3m3

3. Cumulate separately: cum1, cum2, cum3

4. Apply starting points: y = y0 + cum

5. Final adjustment for rolling quarter consistency

See Also

fetch_sidra_rolling_quarters to obtain input data compute_series_starting_points for custom calibration

Examples

rq <- fetch_sidra_rolling_quarters(
  series = c("taxadesocup", "popocup", "popdesocup")
)
monthly <- mensalize_sidra_series(rq)
head(monthly)

PNADCperiods documentation built on April 28, 2026, 9:07 a.m.