Description Usage Arguments Value Examples

`SPE`

conducts SPE estimation and inference at user-specifed quantile index. The bootstrap procedures
follows algorithm 2.1 as in Chernozhukov, Fernandez-Val and Luo (2018). All estimates are bias-corrected
and all confidence bands are monotonized. For graphical results, please use `SPEplot`

.

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`fm` |
Regression formula. |

`data` |
Data in use. |

`method` |
Models to be used for estimating partial effects. Four options: |

`var.type` |
The type of parameter in interest. Three options: |

`var.T` |
Variable T in interset. Should be a character type. |

`compare` |
If parameter in interest is categorical, then user needs to specify which two category to
compare with. Should be a 1 by 2 character vector. For example, if the two levels to compare
with is 1 and 3, then |

`subgroup` |
Subgroup in interest. Default is |

`samp_weight` |
Sampling weight of data. If null then function implements empirical bootstrap.
If data specifies sampling weight, the function implements weighted bootstrap. Input
should be a n by 1 vector, where n denotes sample size. Default is |

`us` |
Percentile of interest for SPE. Should be a vector of values between 0 and 1. Default
is |

`alpha` |
Size for confidence interval. Shoule be between 0 and 1. Default is 0.1 |

`taus` |
Indexes for quantile regression. Default is |

`B` |
Number of bootstrap draws. Default is set to be 10. For more accurate results, we recommend 500. |

`ncores` |
Number of cores for computation. Default is set to be 1. For large dataset, parallel computing is highly recommended since bootstrap is time-consuming. |

`seed` |
Pseudo-number generation for reproduction. Default is 1. |

`bc` |
Whether want the estimate to be bias-corrected. Default is |

`boot.type` |
Type of bootstrap. Default is |

The output is a list with 4 components: (1) `spe`

stores spe estimates and confidence bounds;
(2) `ape`

stores ape estimates and confidence bounds; (3) `us`

stores percentile index as in
`SPE`

command; (4) `alpha`

stores significance level as in `SPE`

command.

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