Declare the recipe's scope: cross-sectional or longitudinal weights
Source:R/spec-steps-panel.R
step_cross_sectional.RdTwo argument-free declarative steps that say what the recipe is building. They
do not touch the weights (like step_assert()); they record the intent so the
rest of the recipe, the report and the estimators behave accordingly, and so a
wrong-purpose estimate can be flagged. All the panel detail – waves, rotation
group, reference wave – already lives in panel_design(), so these steps need
no arguments.
Details
Put the scope step first. step_cross_sectional() is the default (a plain recipe
with no scope step behaves as cross-sectional). step_longitudinal() requires the
data to be tagged by panel_design() and marks the recipe as building the panel
(longitudinal) weight, so steps like step_panel_overlap() apply and the
calibration targets the reference wave.
ECLAC conventions for the longitudinal weight
The three points below are conventions of ECLAC's household-survey manual (ch. XVI). The package cannot enforce them – a vector of control totals carries no label saying which period it belongs to – so they are the reader's to apply.
Who is in the longitudinal population. It is made up of the units that were in the target
population at the first period and remained in it through the last. Units that left
(death, migration, institutionalization) are out, and so are units that entered after the
first period: a panel adds no elements over time. Drop the first group with
step_drop_ineligible() before the attrition step – leaving the target population is not
nonresponse and must not be compensated by redistributing weight.
Calibrate to the FIRST period's totals. ECLAC is explicit that the auxiliary totals used in the calibration must represent the population of the first period of interest, because a panel that adds no elements over the measurement periods is representative only of the period in which it was selected (ch. XVI, sec. B.2.c). In the original:
"los totales auxiliares utilizados en la calibracion deben representar la
poblacion del primer periodo de interes, puesto que, al conformar un panel
que no suma elementos a lo largo de los periodos de medicion, la muestra
sera representativa unicamente del periodo en el que fue seleccionada"Passing the later period's projections is a silent error: the recipe converges, the totals close, and the weights represent a population the sample never had a chance to cover.
Cross-sectional estimates from a longitudinal file are reference only. They will not match the published cross-sectional figures and should not: the target population of the panel combination is not the target population of the cross-section (ch. XVI, sec. C). Use the longitudinal weight for gross flows, transitions and durations – what it exists for – and the cross-sectional weight for levels.
Examples
wide <- panel_merge(
list(T1 = subset(panel_ine, ola == 1), T2 = subset(panel_ine, ola == 2)),
by = c("id_hogar", "nper"), require = "all")
weighting_spec(wide, base_weights = w_base_T1) |>
step_longitudinal() |>
step_panel_overlap(prob = 5 / 6) |> prep()
#> Warning: step_longitudinal() without a panel_design() on the data: the reference wave and rotation structure are unknown. Tag the recipe data with panel_design() for the reference wave, Pr(panel selection) and the panel report.
#>
#> == Weighting specification (weightflow) ==
#> Data : 1717 cases
#> Base wts: w_base_T1
#> Steps :
#> 1. scope: longitudinal [longitudinal_1]
#> 2. panel overlap [panel_overlap_1]
#> Status : estimated (prep)
#>
#> Stage summary:
#> stage n_active sum_wts cv_wts deff_kish n_eff
#> base 1717 245498 0.3 1.09 1575
#> stage_1_step_longitudinal 1717 245498 0.3 1.09 1575
#> stage_2_step_panel_overlap 1717 294597 0.3 1.09 1575
#>
#> deff_kish = 1 + CV^2 (Kish design effect from unequal weighting);
#> n_eff = n_active / deff_kish. Both worsen with each adjustment and
#> improve with trimming.
#>