Convert Rolling Quarters to Exact Monthly Series

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.

Value

A data.table with columns:

anomesexato

Integer. YYYYMM exact month

m_*

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

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.

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

# \donttest{
rq <- fetch_sidra_rolling_quarters(
  series = c("taxadesocup", "popocup", "popdesocup")
)
#> Fetching 3 series from SIDRA API...
#> This may take a few minutes on first run.
#>   [ 33%] Fetching popocup...
#>   [ 67%] Fetching popdesocup...
#>   [100%] Fetching taxadesocup...
#> Successfully fetched 3/3 series
#>   Rolling quarters: 201203 to 202603 (169 observations)
monthly <- mensalize_sidra_series(rq)
#> Using bundled starting points
#> Mensalizing 2 series...
#> Computing derived series...
#>     Computed aggregate: m_popnaforca
#>     Computed rate: m_taxadesocup
#> Mensalization complete: 169 months (201203 to 202603)
head(monthly)
#>    anomesexato m_popocup m_popdesocup m_popnaforca m_taxadesocup
#>          <int>     <num>        <num>        <num>         <num>
#> 1:      201203  88538.06     7375.089     95913.15           7.7
#> 2:      201204  88197.81     7696.149     95893.95           8.0
#> 3:      201205  89066.75     7342.311     96409.06           7.6
#> 4:      201206  90689.45     6918.540     97607.99           7.1
#> 5:      201207  88578.81     7474.149     96052.95           7.8
#> 6:      201208  89927.75     6955.311     96883.06           7.2
# }