There is the continued collinearity in the event study for checking the pre-parallel trend.

[Jun Choi]

Hello Stata community,




I am trying to estimate the new law enacted in South Korea impact on the industrial accidents, which are the death rate and the injury rate.

In order to check the pre-parallel trend for the death rate and the injury rate, I used “eventdd.”, which is made by Stata users in Stata 18.




However, the results I tried failed with the collinearity.

Please refer to my codes and the results I mentioned before.

---

eventdd death_rate post i.year [pweight= mean_worker], timevar(timetotreat) ci(rcap) method(fe, cluster(id))




(sum of wgt is 18,021,670.016541)

(sum of wgt is 18,021,670.016541)

(sum of wgt is 18,021,670.016541)

note: 2019.year omitted because of collinearity

note: 2022.year omitted because of collinearity

note: lead4 omitted because of collinearity

note: lead3 omitted because of collinearity

note: lead2 omitted because of collinearity

note: lag0 omitted because of collinearity




Fixed-effects (within) regression               Number of obs     =        278

Group variable: id                              Number of groups  =         99




R-squared:                                      Obs per group:

     Within  = 0.0001                                         min =          1

     Between = 0.0000                                         avg =        2.8

     Overall = 0.0006                                         max =          6




                                                F(5, 98)          =       1.41

corr(u_i, Xb) = -0.0042                         Prob > F          =     0.2284




                                    (Std. err. adjusted for 99 clusters in id)

------------------------------------------------------------------------------

             |               Robust

  death_rate | Coefficient  std. err.      t    P>|t|     [95% conf. interval]

-------------+----------------------------------------------------------------

        post |   .1113053   .0731395     1.52   0.131    -.0338377    .2564482

             |

        year |

       2018  |  -.0156951   .1819686    -0.09   0.931    -.3768059    .3454157

       2019  |          0  (omitted)

       2020  |  -.0115101   .1112224    -0.10   0.918    -.2322273    .2092072

       2021  |   .0795687   .0817585     0.97   0.333    -.0826785    .2418158

       2022  |          0  (omitted)

             |

       lead5 |  -.2303968   .1721523    -1.34   0.184    -.5720276    .1112339

       lead4 |          0  (omitted)

       lead3 |          0  (omitted)

       lead2 |          0  (omitted)

        lag0 |          0  (omitted)

       _cons |   .7130373   .0682963    10.44   0.000     .5775056     .848569

-------------+----------------------------------------------------------------

     sigma_u |  612.41442

     sigma_e |   12.90711

         rho |  .99955601   (fraction of variance due to u_i)

------------------------------------------------------------------------------

I should include “i.year.” to estimate the causality of my research topic, which is the time effect.

But, whenever I put “i.year.”, the collinearity occurs with the messages in the command window.
Notice that the problem for checking the pre-parallel trend would be the continued collinearity.


How can I do this situation in Stata?

How can I test the pre-parallel trend to use Difference-in-Difference using the “eventdd.”?




I would appreciate it if you could help me estimate the pre-parallel trend for checking the Difference-in-Difference.




Thank you for your help in advance.

Jun

[Carlo Lazzaro]

Jun:
why not using -xtdidregress- and test the parallel tren assumption via -estat trendplots-?
In addition, your within Rsq is dramatically low: I would recommend youi to double-check your model specification.