`ddid2.Rd`

`ddid2`

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

ddid2(formla, xformla = NULL, t, tmin1, tname, data, panel = TRUE, dropalwaystreated = TRUE, idname = NULL, probs = seq(0.05, 0.95, 0.05), iters = 100, alp = 0.05, method = "logit", se = TRUE, retEachIter = FALSE, seedvec = NULL, pl = FALSE, cores = NULL)

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

##load the data data(lalonde) ## Run the ddid2 method on the observational data with no covariates d1 <- ddid2(re ~ treat, t=1978, tmin1=1975, tname="year", data=lalonde.psid.panel, idname="id", se=FALSE, probs=seq(0.05, 0.95, 0.05))#> Warning: dropping 2675 observations that are not in period: 1978, 1975, ...#> Warning: covariates appear to vary over time... #> only conditioning on first period covariates... #> this is recommended practice, but worth noting...summary(d1)#> #> Quantile Treatment Effect: #> #> tau QTE #> 0.05 10616.61 #> 0.1 5019.83 #> 0.15 2388.12 #> 0.2 1033.23 #> 0.25 485.23 #> 0.3 943.05 #> 0.35 931.45 #> 0.4 945.35 #> 0.45 1205.88 #> 0.5 1362.11 #> 0.55 1279.05 #> 0.6 1618.13 #> 0.65 1834.30 #> 0.7 1326.06 #> 0.75 1586.35 #> 0.8 1256.09 #> 0.85 723.10 #> 0.9 251.36 #> 0.95 -1509.92 #> #> Average Treatment Effect: 2326.51## Run the ddid2 method on the observational data with covariates d2 <- ddid2(re ~ treat, t=1978, tmin1=1975, tname="year", data=lalonde.psid.panel, idname="id", se=FALSE, xformla=~age + I(age^2) + education + black + hispanic + married + nodegree, probs=seq(0.05, 0.95, 0.05))#> Warning: dropping 2675 observations that are not in period: 1978, 1975, ...#> Warning: covariates appear to vary over time... #> only conditioning on first period covariates... #> this is recommended practice, but worth noting...#> Warning: Solution may be nonunique#> Warning: Solution may be nonunique#> Warning: Solution may be nonunique#> Warning: Solution may be nonuniquesummary(d2)#> #> Quantile Treatment Effect: #> #> tau QTE #> 0.05 9832.19 #> 0.1 4544.63 #> 0.15 2068.77 #> 0.2 849.12 #> 0.25 485.23 #> 0.3 943.05 #> 0.35 817.00 #> 0.4 681.37 #> 0.45 1010.12 #> 0.5 992.30 #> 0.55 938.13 #> 0.6 950.31 #> 0.65 977.04 #> 0.7 366.22 #> 0.75 563.49 #> 0.8 -352.59 #> 0.85 -825.48 #> 0.9 -1978.06 #> 0.95 -3333.16 #> #> Average Treatment Effect: 1665.63