Description Usage Arguments Value References Examples

`ddid2`

computes the Quantile Treatment Effect
on the Treated (QTET) using the method of Callaway, Li, and Oka (2015).

1 2 3 4 |

`formla` |
The formula y ~ d where y is the outcome and d is the treatment indicator (d should be binary) |

`xformla` |
A optional one sided formula for additional covariates that will be adjusted for. E.g ~ age + education. Additional covariates can also be passed by name using the x paramater. |

`t` |
The 3rd time period in the sample (this is the name of the column) |

`tmin1` |
The 2nd time period in the sample (this is the name of the column) |

`tname` |
The name of the column containing the time periods |

`data` |
The name of the data.frame that contains the data |

`panel` |
Boolean indicating whether the data is panel or repeated cross sections |

`dropalwaystreated` |
How to handle always treated observations in panel data case (not currently used) |

`idname` |
The individual (cross-sectional unit) id name |

`probs` |
A vector of values between 0 and 1 to compute the QTET at |

`iters` |
The number of iterations to compute bootstrap standard errors. This is only used if se=TRUE |

`alp` |
The significance level used for constructing bootstrap confidence intervals |

`method` |
The method for estimating the propensity score when covariates are included |

`se` |
Boolean whether or not to compute standard errors |

`retEachIter` |
Boolean whether or not to return list of results from each iteration of the bootstrap procedure |

`seedvec` |
Optional value to set random seed; can possibly be used in conjunction with bootstrapping standard errors. |

`pl` |
boolean for whether or not to compute bootstrap error in parallel. Note that computing standard errors in parallel is a new feature and may not work at all on Windows. |

`cores` |
the number of cores to use if bootstrap standard errors are computed in parallel |

`QTE`

object

Callaway, Brantly, Tong Li, and Tatsushi Oka. “Quantile Treatment Effects in Difference in Differences Models under Dependence Restrictions and with Only Two Time Periods.” Working Paper, 2015.

1 2 3 4 5 6 7 8 9 10 | ```
##load the data
data(lalonde)
## Run the panel.qtet method on the experimental data with no covariates
pq1 <- ddid2(re ~ treat, t=1978, tmin1=1975, tname="year",
x=NULL, data=lalonde.psid.panel, idname="id", se=FALSE,
probs=seq(0.05, 0.95, 0.05))
summary(pq1)
## Run the panel.qtet method on the observational data with no covariates
``` |

qte documentation built on April 30, 2018, 5:03 p.m.

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