Description Usage Arguments Details Value References Examples

Difference-in-differences-based estimation of the average treatment effect on the treated in the post-treatment period, given a binary treatment with one pre- and one post-treatment period. Permits controlling for differences in observed covariates across treatment groups and/or time periods based on inverse probability weighting.

1 |

`y` |
Dependent variable, must not contain missings. |

`d` |
Treatment, must be binary (either 1 or 0), must not contain missings. |

`t` |
Time period, must be binary, 0 for pre-treatment and 1 for post-treatment, must not contain missings. |

`x` |
Covariates to be controlled for by inverse probability weighting. Default is |

`boot` |
Number of bootstrap replications for estimating standard errors. Default is 1999. |

`trim` |
Trimming rule for discarding observations with extreme propensity scores in the 3 reweighting steps, which reweight (1) treated in the pre-treatment period, (2) non-treated in the post-treatment period, and (3) non-treated in the pre-treatment period according to the covariate distribution of the treated in the post-treatment period. Default is 0.05, implying that observations with a probability lower than 5 percent of not being treated in some weighting step are discarded. |

`cluster` |
A cluster ID for block or cluster bootstrapping when units are clustered rather than iid. Must be numerical. Default is |

Estimation of the average treatment effect on the treated in the post-treatment period based Difference-in-differences. Inverse probability weighting is used to control for differences in covariates across treatment groups and/or over time. That is, (1) treated observations in the pre-treatment period, (2) non-treated observations in the post-treatment period, and (3) non-treated observations in the pre-treatment period are reweighted according to the covariate distribution of the treated observations in the post-treatment period. The respective propensity scores are obtained by probit regressions.

A didweight object contains 4 components, `eff`

, `se`

, `pvalue`

, and `ntrimmed`

.

`eff`

: estimate of the average treatment effect on the treated in the post-treatment period.

`se`

: standard error obtained by bootstrapping the effect.

`pvalue`

: p-value based on the t-statistic.

`ntrimmed`

: total number of discarded (trimmed) observations in any of the 3 reweighting steps due to extreme propensity score values.

Abadie, A. (2005): "Semiparametric Difference-in-Differences Estimators", The Review of Economic Studies, 72, 1-19.

Lechner, M. (2011): "The Estimation of Causal Effects by Difference-in-Difference Methods", Foundations and Trends in Econometrics, 4, 165-224.

1 2 3 4 5 6 7 8 9 10 11 | ```
# A little example with simulated data (4000 observations)
## Not run:
n=4000 # sample size
t=1*(rnorm(n)>0) # time period
u=rnorm(n) # time constant unobservable
x=0.5*t+rnorm(n) # time varying covariate
d=1*(x+u+rnorm(n)>0) # treatment
y=d*t+d+t+x+u # outcome
# The true effect equals 1
didweight(y=y,d=d,t=t,x=x, boot=199)
## End(Not run)
``` |

```
$effect
[1] 0.8895547
$se
[1] 0.09051134
$pvalue
[1] 8.520876e-23
$ntrimmed
[1] 281
```

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.