`ordid`

computes the outcome regressions estimators for the average treatment effect on the
treated in difference-in-differences (DiD) setups. It can be used with panel or repeated cross section data.
See Sant'Anna and Zhao (2020) for details.

ordid( yname, tname, idname, dname, xformla = NULL, data, panel = TRUE, weightsname = NULL, boot = FALSE, boot.type = c("weighted", "multiplier"), nboot = 999, inffunc = FALSE )

yname | The name of the outcome variable. |
---|---|

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

idname | The name of the column containing the unit id name. |

dname | The name of the column containing the treatment group (=1 if observation is treated in the post-treatment, =0 otherwise) |

xformla | A formula for the covariates to include in the model. It should be of the form |

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

panel | Whether or not the data is a panel dataset. The panel dataset should be provided in long format -- that
is, where each row corresponds to a unit observed at a particular point in time. The default is TRUE.
When |

weightsname | The name of the column containing the sampling weights. If NULL, then every observation has the same weights. |

boot | Logical argument to whether bootstrap should be used for inference. Default is |

boot.type | Type of bootstrap to be performed (not relevant if |

nboot | Number of bootstrap repetitions (not relevant if boot = |

inffunc | Logical argument to whether influence function should be returned. Default is |

A list containing the following components:

The IPW DID point estimate

The IPW DID standard error

Estimate of the upper bound of a 95% CI for the ATT

Estimate of the lower bound of a 95% CI for the ATT

All Bootstrap draws of the ATT, in case bootstrap was used to conduct inference. Default is NULL

Estimate of the influence function. Default is NULL

The matched call.

Some arguments used in the call (panel, normalized, boot, boot.type, nboot, type=="or")

The `ordid`

function implements
outcome regression difference-in-differences (DID) estimator for the average treatment effect
on the treated (ATT) defined in equation (2.2) of Sant'Anna and Zhao (2020). The estimator follows the same spirit
of the nonparametric estimators proposed by Heckman, Ichimura and Todd (1997), though here the the outcome regression
models are assumed to be linear in covariates (parametric).

The nuisance parameters (outcome regression coefficients) are estimated via ordinary least squares.

Heckman, James J., Ichimura, Hidehiko, and Todd, Petra E. (1997),"Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme", Review of Economic Studies, vol. 64(4), p. 605–654, doi: 10.2307/2971733 .

Sant'Anna, Pedro H. C. and Zhao, Jun. (2020), "Doubly Robust Difference-in-Differences Estimators." Journal of Econometrics, Vol. 219 (1), pp. 101-122, doi: 10.1016/j.jeconom.2020.06.003

# ----------------------------------------------- # Panel data case # ----------------------------------------------- # Form the Lalonde sample with CPS comparison group eval_lalonde_cps <- subset(nsw_long, nsw_long$treated == 0 | nsw_long$sample == 2) # Implement OR DID with panel data ordid(yname="re", tname = "year", idname = "id", dname = "experimental", xformla= ~ age+ educ+ black+ married+ nodegree+ hisp+ re74, data = eval_lalonde_cps, panel = TRUE)#> Call: #> ordid(yname = "re", tname = "year", idname = "id", dname = "experimental", #> xformla = ~age + educ + black + married + nodegree + hisp + #> re74, data = eval_lalonde_cps, panel = TRUE) #> ------------------------------------------------------------------ #> Outcome-Regression DID estimator for the ATT: #> #> ATT Std. Error t value Pr(>|t|) [95% Conf. Interval] #> -1300.6446 349.8365 -3.7179 2e-04 -1986.3241 -614.965 #> ------------------------------------------------------------------ #> Estimator based on panel data. #> Outcome regression est. method: OLS. #> Analytical standard error. #> ------------------------------------------------------------------ #> See Sant'Anna and Zhao (2020) for details.# ----------------------------------------------- # Repeated cross section case # ----------------------------------------------- # use the simulated data provided in the package # Implement OR DID with repeated cross-section data # use Bootstrap to make inference with 199 bootstrap draws (just for illustration) ordid(yname="y", tname = "post", idname = "id", dname = "d", xformla= ~ x1 + x2 + x3 + x4, data = sim_rc, panel = FALSE, boot = TRUE, nboot = 199)#> Call: #> ordid(yname = "y", tname = "post", idname = "id", dname = "d", #> xformla = ~x1 + x2 + x3 + x4, data = sim_rc, panel = FALSE, #> boot = TRUE, nboot = 199) #> ------------------------------------------------------------------ #> Outcome-Regression DID estimator for the ATT: #> #> ATT Std. Error t value Pr(>|t|) [95% Conf. Interval] #> -8.791 7.127 -1.2335 0.2174 -23.1097 5.5278 #> ------------------------------------------------------------------ #> Estimator based on (stationary) repeated cross-sections data. #> Outcome regression est. method: OLS. #> Boostrapped standard error based on 199 bootstrap draws. #> Bootstrap method: weighted . #> ------------------------------------------------------------------ #> See Sant'Anna and Zhao (2020) for details.