View source: R/do_correction.R
do_correction | R Documentation |
Implements the correction for the integration constraint.
do_correction( zstar, binwidth, data_prepped, firstpass_results, correct_iter_max = 200, notch = FALSE, zD_bin = NA )
zstar |
a numeric value for the the bunching point. |
binwidth |
a numeric value for the width of each bin. |
data_prepped |
(binned) data that includes all variables necessary for fitting the model. |
firstpass_results |
initial bunching estimates without correction. |
correct_iter_max |
maximum iterations for integration constraint correction. Default is 200. |
notch |
whether analysis is for a kink or notch. Default is FALSE (kink). |
zD_bin |
the bin marking the upper end of the dominated region (notch case). |
do_correction returns a list with the data and estimates after correcting for the integration constraint, as follows:
data |
The dataset with the corrected counterfactual. |
coefficients |
The coefficients of the model fit on the corrected data. |
b_corrected |
The normalized excess mass, corrected for the integration constraint. |
B_corrected |
The excess mass (not normalized), corrected for the integration constraint. |
c0_corrected |
The counterfactual at zstar, corrected for the integration constraint. |
marginal_buncher_corrected |
The location (z value) of the marginal buncher, corrected for the integration constraint. |
alpha_corrected |
The estimated fraction of bunchers in the dominated region, corrected for the integration constraint (only in notch case). |
bunchit
, fit_bunching
data(bunching_data) binned_data <- bin_data(z_vector = bunching_data$kink, zstar = 10000, binwidth = 50, bins_l = 20, bins_r = 20) prepped_data <- prep_data_for_fit(binned_data, zstar = 10000, binwidth = 50, bins_l = 20, bins_r = 20, poly = 4) firstpass <- fit_bunching(prepped_data$data_binned, prepped_data$model_formula, binwidth = 50) corrected <- do_correction(zstar = 10000, binwidth = 50, data_prepped = prepped_data$data_binned, firstpass_results = firstpass) paste0("Without correction, b = ", firstpass$b_estimate) paste0("With correction, b = ", round(corrected$b_corrected,3))
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