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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