PSM with panel data

[shahla ebrahimi]

I would greatly appreciate if you could let me know how to do PSM with panel data. In fact, I should do difference in difference (DID) followed by PSM.

• PSM (probit one-for-one match without replacement):

  . probit switch big4 lnasset leverage loss

• DID:

  . reg decost switch post_switch switch*post_switch lnaudten big4 altmanz lnasset lnage markettobook leverage profit tangible cashvol

If I reshape my data into wide format, I would encounter the issues discussed here. Therefore, I decided to implement a year-by-year PSM as suggested here.
However, since the exact structure of dataset is not provided, I couldn’t understand how the related codes work. For example, what is “matched” ?
Please find some part of my dataset here.

Thanks in advance.
Best regards,

[shahla ebrahimi]

Some part of my data are as follows:

id date lnaudten big4 altmanz lnasset lnage    mtob     lev    prof   tang   cavol  switch decost los
1  86  .693147    0   18.4373 12.4689 2.48491 3.69137 .051575 .44427  .999581 .195047  0 .205964  0
1  87  1.09861    0   12.5244 12.7628 2.56495 2.69891 .043572 .559291 .999688 .128583  0 .107817  0
1  88  1.38629    0   14.7922 13.3187 2.63906 3.55144 .037377 .901665 .99897  .045367  0 .085176  0
1  89  1.60944    0   21.6806 13.5282 2.70805 4.4521  .090386 1.00277 .998904 .034365  0 .059932  0
1  90  1.79176    0   16.6034 13.7204 2.77259 3.16585 .077934 1.21371 .999292 .032229  0 .064589  0
1  91  0          0   9.32285 14.0652 2.83321 1.87682 .038984 1.61792 .999376 .019715  1 .086323  0
1  92  .693147    0   29.1306 14.3805 2.89037 3.83173 .030874 3.42558 .999687 .117503  0 .148985  0
1  93  1.09861    0   23.7929 14.5855 2.94444 3.08877 .01225  4.19413 .999862 .171374  0 .181363  0
2  86  1.94591    1   2.67142 13.5351 1.60944 .90438  .031392 .284566 .997711 .172729  0 .116186  0
2  87  2.07944    1   1.85554 13.6068 1.79176 .783169 .037099 .28575  .997862 .055812  0 .137087  0
2  88  2.19723    1   3.25227 13.6162 1.94591 .857463 .046493 .264266 .99788  .052991  0 .174771  0
2  89  2.30258    1   2.46358 13.8247 2.07944 1.00449 .045589 .246997 .998208 .064097  0 .168786  0
2  90  2.3979     1   1.43551 13.8304 2.19723 .791431 .060575 .171494 .998218 .062911  0 .240464  0
2  91  0          0   1.10687 13.7423 2.30258 .532189 .071249 .164944 .998054 .093181  1 .351773  0
2  92  .693147    0   3.39252 13.8668 2.3979  1.80869 .121138 .177533 .998281 .090341  0 .282046  0
2  93  1.09861    0   3.95825 14.0244 2.48491 1.41083 .094626 .162305 .99847  .134091  0 .188627  0
3  86  .693147    0   5.01935 13.0392 3.49651 1.08849 .008833 .275658 .995814 .165765  0 .12684   0
3  87  1.09861    0   8.51978 13.0429 3.52636 .794968 .010574 .349996 .995351 .276396  0 2.49701  0
3  88  1.38629    0   13.1943 13.2777 3.55535 1.36713 .043884 .409195 .996392 .079824  0 .033575  0
3  89  1.60944    0   18.7427 13.4562 3.58352 1.89782 .010373 .42366  .997045 .049833  0 .057621  0
3  90  1.79176    0   20.2185 13.4667 3.61092 1.69264 .016154 .339384 .997148 .133837  0 .133177  0
3  91  0          0   11.1153 13.9098 3.63759 1.50931 .010464 .935899 .998216 .12095   1 .089572  0
3  92  .693147    0   25.7134 14.1341 3.66356 2.41058 .004609 1.06214 .99856  .13175   0 .171943  0
3  93  1.09861    0   29.8983 14.162  3.68888 2.29729 .003891 .902802 .997648 .146949  0 .823985  0

[Jorge Eduardo Perez Perez]

Hi Shahla. Could you be more specific about the code you used to implement the propensity score matching? All I see here is the probit and the regression, but I don't see any matching and the regression is not estimated on a matched sample.

[shahla ebrahimi]

Jorge Eduardo Perez Perez Hi Dr. Perez. Sorry for being late. Really, I read this but I couldn't understand how to do it for my dataset. In my dataset, the treatment dates are different for each firm. Besides, the treatment could occur more the once for each firm. Therefore, I don’t know how to define

post_switch

variable.

Exactly speaking, I just know how to do PSM for cross-sectional data.
Thanks in advance,

[Jorge Eduardo Perez Perez]

I am sorry but the question is still too unclear for me to be able to help. If the treatment dates are different for each firm and treatments are repeated, then you could define your time variable as time to treatment in an event-study fashion, then do propensity score matching between treatment and control for the dates leading up to the treatment.