bunch {bunchr} | R Documentation |

Given a kinked or notched budget set, this function gets a vector of earnings and analyzes bunching. The bunchr package has two main useful functions:

bunch(earnings, zstar, t1, t2, Tax = 0, cf_start = NA, cf_end = NA, exclude_before = NA, exclude_after = NA, force_after = FALSE, binw = 10, poly_size = 7, convergence = 0.01, max_iter = 100, correct = TRUE, select = TRUE, draw = TRUE, nboots = 0, seed = NA, progress = FALSE, title = "Bunching Visualization", varname = "Earnings")

`earnings` |
Vector of earnings, hopefully a very large one. |

`zstar` |
Place of kink (critical earning point). |

`t1` |
Marginal tax rate before kink. |

`t2` |
Marginal tax rate after kink. |

`Tax` |
"Penalty" tax for crossing zstar. |

`cf_start` |
Number of bins before the kink bin where counter-factual histogram should start. |

`cf_end` |
Number of bins after the kink bin where counter-factual histogram should start. |

`exclude_before` |
Number of excluded bins before the kink bin. |

`exclude_after` |
Number of excluded bins after the kink bin. |

`force_after` |
For notch analysis, should |

`binw` |
Bin width. |

`poly_size` |
Order of polynomial used to calculate counter-factual histogram. |

`convergence` |
Minimal rate of change of bunching estimate to stop iterations. |

`max_iter` |
Maximum number of iterations for bunching estimates. |

`correct` |
Should the counter-factual histogram be corrected to compensate for shifting left because of the notch? See details. |

`select` |
Should model selection be used to find counter-factual histogram? See details. |

`draw` |
Should a graph be drawn? |

`nboots` |
how many bootstraps should be run? |

`seed` |
specify seed for bootstraps (earnings sampling). |

`progress` |
Should a progress bar be desplayed? |

`title` |
Title for Plot output |

`varname` |
Name for running variable, to be desplayed in the plot |

`bunch`

checks if the specification has a kink, i.e. if the Tax
parameter is greater than zero. If so, it applies `notch_estimator`

.
Otherwise, it applies `kink_estimator`

. Additionally, `bunch`

can bootstrap by sampling the earnings vector, returning a vector with
the estimated elasticities.

`bunch`

returns a list comprising of the parameters returned by
`kink_estimator`

and `notch_estimator`

. If bootstraps were asked for,
bootstrapped values are added to the list. Drawing of histograms is
suppressed when running the bootsraps.

`kink_estimator`

, `notch_estimator`

# analyzing a kink ability_vec <- 4000 * rbeta(100000, 2, 5) earning_vec <- sapply(ability_vec, earning_fun, 0.2, 0, 0.2, 0, 1000) # bunch_viewer(earning_vec, 1000, 20, 20, 1, 1, binw = 20) estim <- bunch(earning_vec, 1000, 0, 0.2, Tax = 0, 20, 20, 1, 1, binw = 20, draw=TRUE, nboots = 0, seed = 16) estim$e # analyzing a notch earning_vec <- sapply(ability_vec, earning_fun, 0.2, 0.2, 0.2, 500, 1000) bunch_viewer(earning_vec, 1000, 10, 40, 2, 22, binw = 50) estim <- bunch(earning_vec, 1000, 0.2, 0.2, Tax = 500, 10, 40, 2, 22, binw = 50, draw = FALSE, nboots = 0, seed = 16) estim$e

[Package *bunchr* version 1.2.0 Index]