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  • #1

    Difference-in-difference (DiD)

    Hello,

    I'm having difficulty running a difference-in-difference (DiD) test in stata and was hoping someone could offer some advice.

    Below is subset of my data.

    My data includes:
    Volume = the number of equities traded in a particular minute
    Pre_or_Post_Colo = a flag with a zero if the trades occurred before a certain date (pre) and a 1 if they occurred after a certain date (post)
    Flag_day_before_ann_1 = a flag of 1 represents trading on a day prior to an announcement for that stock, and a zero represents the day an announcement was made
    PriceSensitive3 = a flag of zero if for that trade it there was no announcement, 1 if there was a price sensitive market announcement and and flag of 2 if there was a non price sensitive announcement

    I'm trying to assess a DiD for the following:
    1 - Volume, pre, on the day of an announcement compared to the day prior, depending on whether there was no announcements a price sensitive announcement or a non price sensitive announcement
    2 - Volume, post, on the day of an announcement compared to the day prior, depending on whether there was no announcements a price sensitive announcement or a non price sensitive announcement
    - The DiD between 1 and 2

    I cant seem to work out the command to do this either using the command line and or the built in function. Can a forum member offer some advice please?

    Thanks


    -------------------------------------
    input double(Volume Pre_or_Post_Colo) byte Flag_day_before_ann_1 float PriceSensitive3
    34 0 0 0
    2105 0 0 0
    11 0 0 0
    8492 0 0 0
    23 0 0 0
    1217 0 0 0
    4743 0 0 0
    1017 0 0 0
    375 0 0 0
    154 0 0 0
    2018 0 0 0
    3449 0 0 0
    10 0 0 0
    40 0 0 0
    1668 0 0 0
    162 0 0 0
    614 0 0 0
    10172 0 0 0
    43 0 0 0
    86 0 0 0
    5969 0 0 0
    2499 0 0 0
    116 0 0 0
    5227 0 0 0
    105 0 0 0
    9406 0 0 0
    384 0 0 0
    3 0 0 0
    2752 0 0 0
    27 0 0 0
    4931 0 0 0
    9805 0 0 0
    24028 0 0 0
    15 0 0 0
    5356 0 0 0
    2000 0 0 0
    196 0 0 0
    27 0 0 0
    785 0 0 0
    1583 0 0 0
    3000 0 0 0
    47168 0 0 0
    10768 0 0 0
    407 0 0 0
    12485 0 0 0
    553 0 0 0
    934 0 0 0
    5656 0 0 0
    128 0 0 0
    713 0 0 0
    500 0 0 0
    1 0 0 0
    773 0 0 0
    95106 0 0 0
    7440 0 0 0
    36 0 0 0
    54944 0 0 0
    6641 0 0 0
    9031 0 0 0
    11702 0 0 0
    40796 0 0 0
    31 0 0 0
    3556 0 0 0
    11148 0 0 0
    248 0 0 0
    11649 0 0 0
    3349 0 0 0
    82 0 0 0
    9342 0 0 0
    458 0 0 0
    2254 0 0 0
    112000 0 0 0
    10 0 0 0
    3097 0 0 0
    37121 0 0 0
    483 0 0 0
    21118 0 0 0
    49 0 0 0
    107 0 0 0
    901398 0 0 0
    1747 0 0 0
    41 0 0 0
    142048 0 0 0
    1078 0 0 0
    683 0 0 0
    86 0 0 0
    216 0 0 0
    10154 0 0 0
    1759 0 0 0
    330903 0 0 0
    315 0 0 0
    5 0 0 0
    11425 0 0 0
    2236 0 0 0
    5766 0 0 0
    4547 0 0 0
    419 0 0 0
    445 0 0 0
    359 0 0 0
    283 0 0 0
    end
    Tags: None
  • #2
    I'm having difficulty
    What is giving you difficulty specifically? In order words, what code have you tried so far that hasn't given you what you expected? Or, what errors has Stata reported to you?

    Comment

    • #3
      Thanks Jared - I tried a few different combinations, which manly highlighted to me that I was guessing rather than knowing how to conduct the analysis. My attempts are listed below. In the ones below I used a time variable which was 'local_Day' but I didnt think I need to use that if I have a field that denotes trades that occurred in the pre period compared to those that occurred in the post period. Essentially though, I would like to conduct the test as noted in my initial query.


      . xtdidregress (Volume) (Pre_or_Post_Colo), group(PriceSensitive2) time(local_Day)
      variable test_group_1 not found
      r(111);

      . didregress (Volume) (Pre_or_Post_Colo), group(PriceSensitive3) time(local_Day)
      invalid group specification
      None of the groups defined by PriceSensitive3 is a control.
      r(198);

      Comment

      • #4
        Thanks Jared - I tried a few different combinations, which manly highlighted to me that I was guessing rather than knowing how to conduct the analysis. My attempts are listed below. In the ones below I used a time variable which was 'local_Day' but I didnt think I need to use that if I have a field that denotes trades that occurred in the pre period compared to those that occurred in the post period. Essentially though, I would like to conduct the test as noted in my initial query.


        . xtdidregress (Volume) (Pre_or_Post_Colo), group(PriceSensitive2) time(local_Day)
        variable test_group_1 not found
        r(111);

        . didregress (Volume) (Pre_or_Post_Colo), group(PriceSensitive3) time(local_Day)
        invalid group specification
        None of the groups defined by PriceSensitive3 is a control.
        r(198);

        Comment

        • #5
          Just so I can have a better sense of what your data look like, can you repost your dataex example while keeping your time variable local_Day? In your example, none of your units (here) are treated at all, they're all 0s

          Comment

          • #6
            Hi Jared,

            In addition to the data above, below are some further extracts.

            Volume, can be any positive number ie the number of shares traded
            Pre_or_post, can only be 0 or 1. Zero for pre and 1 for post the key date
            Flag_day_before, can be 0 or 1. Zero means the day of an announcement was made and 1 is the day prior
            Pricesensitive3, 0 = no announcement, 1 = price sensitive, 2 = non price sensitive

            Code:
            * Example generated by -dataex-. For more info, type help dataex
clear
input long local_Day double(Volume Pre_or_Post_Colo) byte Flag_day_before_ann_1 float PriceSensitive3
18883 2002987 0 1 2
18786    5672 0 1 2
18966    1071 0 1 2
18794     654 0 1 2
19032   12339 0 1 2
18865    4778 0 1 2
18801    1068 0 1 2
18891   12951 0 1 2
18898    4294 0 1 2
19040      37 0 1 2
18855    6450 0 1 2
18980   36591 0 1 2
18737   13091 0 1 2
18826      60 0 1 2
18752     976 0 1 2
18898      32 0 1 2
18878   12276 0 1 2
18865    1255 0 1 2
18949      79 0 1 2
18899    4313 0 1 2
18862    1235 0 1 2
18697     141 0 1 2
18815     162 0 1 2
18794    4550 0 1 2
18863      76 0 1 2
18920    1477 0 1 2
18779     232 0 1 2
18688   11838 0 1 2
18974    2874 0 1 2
18717     436 0 1 2
18877    5576 0 1 2
18788   31158 0 1 2
18983      93 0 1 2
18945      11 0 1 2
18816    1817 0 1 2
18910  336582 0 1 2
18717     102 0 1 2
18828    5227 0 1 2
18926    4314 0 1 2
18892  255894 0 1 2
18953    2114 0 1 2
18723    2702 0 1 2
18917   14610 0 1 2
18834   35842 0 1 2
19001      79 0 1 2
19039     100 0 1 2
18758  108374 0 1 2
18885    7203 0 1 2
18941    5453 0 1 2
18801    1854 0 1 2
18821     195 0 1 2
18735     823 0 1 2
18717   10765 0 1 2
18771   19918 0 1 2
18896   12651 0 1 2
18689    9948 0 1 2
18701   13186 0 1 2
18890    2483 0 1 2
18961    2287 0 1 2
18913   18956 0 1 2
18753   26317 0 1 2
18872    6652 0 1 2
18864   26746 0 1 2
18896    3858 0 1 2
18864    9525 0 1 2
18679       6 0 1 2
18952   26400 0 1 2
18917     109 0 1 2
18679    7215 0 1 2
18940      89 0 1 2
18836       1 0 1 2
19033     951 0 1 2
18857  118817 0 1 2
18899     133 0 1 2
19040   23970 0 1 2
18802     420 0 1 2
18718       2 0 1 2
18765   11261 0 1 2
18807   21458 0 1 2
18904    6578 0 1 2
18865    1659 0 1 2
18907    1915 0 1 2
18885    1861 0 1 2
18856     394 0 1 2
18998    8693 0 1 2
19038    2639 0 1 2
18863    6578 0 1 2
18899   63095 0 1 2
18877     669 0 1 2
18945     358 0 1 2
18945     632 0 1 2
18890      46 0 1 2
18701    2024 0 1 2
18688      25 0 1 2
18954    8975 0 1 2
18924   16667 0 1 2
18884    3686 0 1 2
18983    2050 0 1 2
19033    4458 0 1 2
18893   32453 0 1 2
end
format %tdD_m_Y local_Day

            input long local_Day double(Volume Pre_or_Post_Colo) byte Flag_day_before_ann_1 float PriceSensitive3
            19345 1429 1 0 0
            19248 3 1 0 0
            19142 2783 1 0 0
            19064 1962 1 0 0
            19281 1348 1 0 0
            19044 3591 1 0 0
            19235 245 1 0 0
            19319 2297 1 0 0
            19080 5898 1 0 0
            19110 211 1 0 0
            19368 1436 1 0 0
            19347 2 1 0 0
            19088 178 1 0 0
            19068 16009 1 0 0
            19047 13994 1 0 0
            19205 48926 1 0 0
            19374 24952 1 0 0
            19156 750 1 0 0
            19156 10 1 0 0
            19121 1363 1 0 0
            19263 4148 1 0 0
            19130 35 1 0 0
            19374 4086 1 0 0
            19152 124 1 0 0
            19183 21440 1 0 0
            19374 141 1 0 0
            19373 4423 1 0 0
            19228 12738 1 0 0
            19134 83 1 0 0
            19192 442 1 0 0
            19344 29021 1 0 0
            19341 194 1 0 0
            19061 3285 1 0 0
            19285 17422 1 0 0
            19115 306 1 0 0
            19320 6471 1 0 0
            19178 15017 1 0 0
            19402 1074 1 0 0
            19200 18454 1 0 0
            19241 1014 1 0 0
            19165 2735 1 0 0
            19121 450 1 0 0
            19061 606 1 0 0
            19109 2913 1 0 0
            19075 78735 1 0 0
            19396 1521 1 0 0
            19365 263 1 0 0
            19242 1463 1 0 0
            19050 1353 1 0 0
            19332 166 1 0 0
            19263 129 1 0 0
            19201 122 1 0 0
            19110 3872 1 0 0
            19235 2714 1 0 0
            19115 3608 1 0 0
            19081 1780 1 0 0
            19191 235 1 0 0
            19344 51 1 0 0
            19372 451 1 0 0
            19375 3333 1 0 0
            19213 4179 1 0 0
            19121 438 1 0 0
            19179 1706 1 0 0
            19045 1307 1 0 0
            19148 2515 1 0 0
            19121 21 1 0 0
            19179 640 1 0 0
            19117 1009 1 0 0
            19095 45 1 0 0
            19212 2051 1 0 0
            19197 1059 1 0 0
            19312 300 1 0 0
            19187 65277 1 0 0
            19138 14886 1 0 0
            19136 81155 1 0 0
            19085 1577 1 0 0
            19306 191 1 0 0
            19326 1310 1 0 0
            19115 30 1 0 0
            19400 78 1 0 0
            19341 225 1 0 0
            19306 9 1 0 0
            19310 1957 1 0 0
            19310 485 1 0 0
            19299 1421 1 0 0
            19130 400 1 0 0
            19331 42690 1 0 0
            19270 6 1 0 0
            19096 12269 1 0 0
            19135 53 1 0 0
            19379 73403 1 0 0
            19381 5830 1 0 0
            19131 296 1 0 0
            19073 13018 1 0 0
            19348 1689 1 0 0
            19283 6 1 0 0
            19151 2731 1 0 0
            19201 25623 1 0 0
            19228 15195 1 0 0
            19059 14109 1 0 0
            end
            format %tdD_m_Y local_Day


            Comment

            • #7
              So wait, I'm confused now. What's your unique identifier variable for your units here?

              Comment

              • #8
                Jared, I dont know what a unique identifier variable for my units is'. I do have a variable that tags the pre effect and the post effect and that variable is called Pre_or_post. I also have another variable that tags the volume on the day of an announcement (Flag_day_before_ann_1 == 0) and the volume the day before when there was no announcement (Flag_day_before_ann_1==1). Is that what you mean?

                Comment

                • #9
                  Hi - Any thoughts Jared?

                  Comment

                  • #10
                    What is your unit of analysis? If I wanted to know how a city raising the minimum wage affects unemployment, my unit of analysis is the city level, that is the level I am collecting my metrics at to do my study.

                    What is your level/unit of analysis?

                    Comment

                    • #11
                      Thanks. From your example you are looking at how the relationship between the city and the min wage, affects unemployment. And to expand on that, a difference-in-difference could be done comparing unemployment between the cities of say Sydney and Melbourne, in 2010 vs 2022, is that right?

                      Using that thinking my case is looking at how the relationship between a stock announcement (1 or 0) with the day before, affects volume traded. The difference-in-difference could then be the volume traded on announcement days, in 2010 vs 2022. If I have understood your analogy correctly, and translated it to my case, then the unit of analysis is 'stock announcement'.

                      Apologies if there is too much data below.

                      Thanks

                      Code:
                      * Example generated by -dataex-. For more info, type help dataex
clear
input str6 Security long local_Day double(Volume Pre_or_Post_Colo) float PriceSensitive3 byte Flag_day_before_ann_1
"AAD" 18679 11292 0 0 0
"AAD" 18679 30731 0 0 0
"AAD" 18679  1084 0 0 0
"AAD" 18679  6146 0 0 0
"AAD" 18679  1936 0 0 0
"AAD" 18679  1747 0 0 0
"AAD" 18679   278 0 0 0
"AAD" 18679  7319 0 0 0
"AAD" 18679 26716 0 0 0
"AAD" 18679   399 0 0 0
"AAD" 18679  2722 0 0 0
"AAD" 18679   356 0 0 0
"AAD" 18679  3696 0 0 0
"AAD" 18679  2366 0 0 0
"AAD" 18679   289 0 0 0
"AAD" 18679  1100 0 0 0
"AAD" 18679  2337 0 0 0
"AAD" 18679  3201 0 0 0
"AAD" 18679   325 0 0 0
"AAD" 18679  1460 0 0 0
"AAD" 18679   322 0 0 0
"AAD" 18679  2426 0 0 0
"AAD" 18679   278 0 0 0
"AAD" 18679   925 0 0 0
"AAD" 18679 23544 0 0 0
"AAD" 18679  5428 0 0 0
"AAD" 18679   281 0 0 0
"AAD" 18679  1738 0 0 0
"AAD" 18679 19759 0 0 0
"AAD" 18679   370 0 0 0
"AAD" 18679  4199 0 0 0
"AAD" 18679  2788 0 0 0
"AAD" 18679  3492 0 0 0
"AAD" 18679   444 0 0 0
"AAD" 18679  4873 0 0 0
"AAD" 18679  1249 0 0 0
"AAD" 18679  1595 0 0 0
"AAD" 18679    73 0 0 0
"AAD" 18679  2537 0 0 0
"AAD" 18679  5298 0 0 0
"AAD" 18679  1976 0 0 0
"AAD" 18679   993 0 0 0
"AAD" 18679  2061 0 0 0
"AAD" 18679   306 0 0 0
"AAD" 18679  4434 0 0 0
"AAD" 18679  1260 0 0 0
"AAD" 18679  2482 0 0 0
"AAD" 18679   743 0 0 0
"AAD" 18679  2043 0 0 0
"AAD" 18679  2254 0 0 0
"AAD" 18679   680 0 0 0
"AAD" 18679  1815 0 0 0
"AAD" 18679  3033 0 0 0
"AAD" 18679    74 0 0 0
"AAD" 18679  1816 0 0 0
"AAD" 18679    88 0 0 0
"AAD" 18679  2159 0 0 0
"AAD" 18679   261 0 0 0
"AAD" 18679  2114 0 0 0
"AAD" 18679  1899 0 0 0
"AAD" 18679   500 0 0 0
"AAD" 18679  1728 0 0 0
"AAD" 18679  2403 0 0 0
"AAD" 18679   986 0 0 0
"AAD" 18679  1115 0 0 0
"AAD" 18679   690 0 0 0
"AAD" 18679 33270 0 0 0
"AAD" 18679   218 0 0 0
"AAD" 18679  1707 0 0 0
"AAD" 18679    75 0 0 0
"AAD" 18679  1632 0 0 0
"AAD" 18679  2530 0 0 0
"AAD" 18679   460 0 0 0
"AAD" 18679  1331 0 0 0
"AAD" 18679   436 0 0 0
"AAD" 18679  2244 0 0 0
"AAD" 18679    21 0 0 0
"AAD" 18679  1429 0 0 0
"AAD" 18679  2306 0 0 0
"AAD" 18679  6747 0 0 0
"AAD" 18679  2258 0 0 0
"AAD" 18679  2416 0 0 0
"AAD" 18679  1818 0 0 0
"AAD" 18679   292 0 0 0
"AAD" 18679  2048 0 0 0
"AAD" 18679 16296 0 0 0
"AAD" 18679 51447 0 0 0
"AAD" 18679 11891 0 0 0
"AAD" 18679    76 0 0 0
"AAD" 18679  2697 0 0 0
"AAD" 18679   141 0 0 0
"AAD" 18679   213 0 0 0
"AAD" 18679  1604 0 0 0
"AAD" 18679   180 0 0 0
"AAD" 18679  1996 0 0 0
"AAD" 18679 11154 0 0 0
"AAD" 18679  1323 0 0 0
"AAD" 18679   638 0 0 0
"AAD" 18679    18 0 0 0
"AAD" 18679  1323 0 0 0
end
format %tdD_m_Y local_Day
                      ------------------ copy up to and including the previous line ------------------

                      Listed 100 out of 29318181 observations
                      Use the count() option to list more

                      . do "C:\Users\Imtiaz\AppData\Local\Temp\STD130c_00 0000 .tmp"

                      . dataex Security local_Day Volume Pre_or_Post_Colo PriceSensitive3 Flag_day_before_ann_1 if Pre_or_Post_Colo==1

                      ----------------------- copy starting from the next line -----------------------
                      Code:
                      * Example generated by -dataex-. For more info, type help dataex
clear
input str6 Security long local_Day double(Volume Pre_or_Post_Colo) float PriceSensitive3 byte Flag_day_before_ann_1
"AAD" 19043   478 1 0 0
"AAD" 19043   434 1 0 0
"AAD" 19043   393 1 0 0
"AAD" 19043   438 1 0 0
"AAD" 19043   576 1 0 0
"AAD" 19043   486 1 0 0
"AAD" 19043   370 1 0 0
"AAD" 19043   509 1 0 0
"AAD" 19043   912 1 0 0
"AAD" 19043   501 1 0 0
"AAD" 19043  1718 1 0 0
"AAD" 19043   508 1 0 0
"AAD" 19043   647 1 0 0
"AAD" 19043   576 1 0 0
"AAD" 19043  1670 1 0 0
"AAD" 19043  5469 1 0 0
"AAD" 19043 11417 1 0 0
"AAD" 19043   569 1 0 0
"AAD" 19043   377 1 0 0
"AAD" 19043   734 1 0 0
"AAD" 19043  1066 1 0 0
"AAD" 19043 12500 1 0 0
"AAD" 19043   374 1 0 0
"AAD" 19043   469 1 0 0
"AAD" 19043    94 1 0 0
"AAD" 19043   723 1 0 0
"AAD" 19043   151 1 0 0
"AAD" 19043   607 1 0 0
"AAD" 19043  3608 1 0 0
"AAD" 19043   492 1 0 0
"AAD" 19043   305 1 0 0
"AAD" 19043   123 1 0 0
"AAD" 19043 43512 1 0 0
"AAD" 19043   503 1 0 0
"AAD" 19043  4239 1 0 0
"AAD" 19043 30000 1 0 0
"AAD" 19043  8180 1 0 0
"AAD" 19043    85 1 0 0
"AAD" 19043 12414 1 0 0
"AAD" 19043   259 1 0 0
"AAD" 19043   308 1 0 0
"AAD" 19043   587 1 0 0
"AAD" 19043   496 1 0 0
"AAD" 19043   930 1 0 0
"AAD" 19043   232 1 0 0
"AAD" 19043   150 1 0 0
"AAD" 19043 10000 1 0 0
"AAD" 19043  1327 1 0 0
"AAD" 19043   596 1 0 0
"AAD" 19043   758 1 0 0
"AAD" 19043   977 1 0 0
"AAD" 19043    96 1 0 0
"AAD" 19043  2765 1 0 0
"AAD" 19043   497 1 0 0
"AAD" 19043   676 1 0 0
"AAD" 19043   246 1 0 0
"AAD" 19043   806 1 0 0
"AAD" 19043   664 1 0 0
"AAD" 19043   439 1 0 0
"AAD" 19043   628 1 0 0
"AAD" 19043 10000 1 0 0
"AAD" 19043  1076 1 0 0
"AAD" 19043  3157 1 0 0
"AAD" 19043   398 1 0 0
"AAD" 19043   559 1 0 0
"AAD" 19043   457 1 0 0
"AAD" 19043   285 1 0 0
"AAD" 19043   173 1 0 0
"AAD" 19043   287 1 0 0
"AAD" 19043   259 1 0 0
"AAD" 19043   112 1 0 0
"AAD" 19043   183 1 0 0
"AAD" 19043   166 1 0 0
"AAD" 19043   261 1 0 0
"AAD" 19043 10000 1 0 0
"AAD" 19043   181 1 0 0
"AAD" 19043   255 1 0 0
"AAD" 19043   847 1 0 0
"AAD" 19043   444 1 0 0
"AAD" 19043   168 1 0 0
"AAD" 19043   259 1 0 0
"AAD" 19043   123 1 0 0
"AAD" 19043   297 1 0 0
"AAD" 19043   154 1 0 0
"AAD" 19043  3090 1 0 0
"AAD" 19043   384 1 0 0
"AAD" 19043 10277 1 0 0
"AAD" 19043   140 1 0 0
"AAD" 19043   212 1 0 0
"AAD" 19043   265 1 0 0
"AAD" 19043   302 1 0 0
"AAD" 19043  3814 1 0 0
"AAD" 19043   520 1 0 0
"AAD" 19043   502 1 0 0
"AAD" 19043 10179 1 0 0
"AAD" 19043   164 1 0 0
"AAD" 19043   676 1 0 0
"AAD" 19043   596 1 0 0
"AAD" 19043   120 1 0 0
"AAD" 19043  1129 1 0 0
end
format %tdD_m_Y local_Day
                      ------------------ copy up to and including the previous line ------------------

                      Listed 100 out of 29318181 observations
                      Use the count() option to list more

                      . dataex Security local_Day Volume Pre_or_Post_Colo PriceSensitive3 Flag_day_before_ann_1 if PriceSensitive3==0

                      ----------------------- copy starting from the next line -----------------------
                      Code:
                      * Example generated by -dataex-. For more info, type help dataex
clear
input str6 Security long local_Day double(Volume Pre_or_Post_Colo) float PriceSensitive3 byte Flag_day_before_ann_1
"AAD" 18679 11292 0 0 0
"AAD" 18679 30731 0 0 0
"AAD" 18679  1084 0 0 0
"AAD" 18679  6146 0 0 0
"AAD" 18679  1936 0 0 0
"AAD" 18679  1747 0 0 0
"AAD" 18679   278 0 0 0
"AAD" 18679  7319 0 0 0
"AAD" 18679 26716 0 0 0
"AAD" 18679   399 0 0 0
"AAD" 18679  2722 0 0 0
"AAD" 18679   356 0 0 0
"AAD" 18679  3696 0 0 0
"AAD" 18679  2366 0 0 0
"AAD" 18679   289 0 0 0
"AAD" 18679  1100 0 0 0
"AAD" 18679  2337 0 0 0
"AAD" 18679  3201 0 0 0
"AAD" 18679   325 0 0 0
"AAD" 18679  1460 0 0 0
"AAD" 18679   322 0 0 0
"AAD" 18679  2426 0 0 0
"AAD" 18679   278 0 0 0
"AAD" 18679   925 0 0 0
"AAD" 18679 23544 0 0 0
"AAD" 18679  5428 0 0 0
"AAD" 18679   281 0 0 0
"AAD" 18679  1738 0 0 0
"AAD" 18679 19759 0 0 0
"AAD" 18679   370 0 0 0
"AAD" 18679  4199 0 0 0
"AAD" 18679  2788 0 0 0
"AAD" 18679  3492 0 0 0
"AAD" 18679   444 0 0 0
"AAD" 18679  4873 0 0 0
"AAD" 18679  1249 0 0 0
"AAD" 18679  1595 0 0 0
"AAD" 18679    73 0 0 0
"AAD" 18679  2537 0 0 0
"AAD" 18679  5298 0 0 0
"AAD" 18679  1976 0 0 0
"AAD" 18679   993 0 0 0
"AAD" 18679  2061 0 0 0
"AAD" 18679   306 0 0 0
"AAD" 18679  4434 0 0 0
"AAD" 18679  1260 0 0 0
"AAD" 18679  2482 0 0 0
"AAD" 18679   743 0 0 0
"AAD" 18679  2043 0 0 0
"AAD" 18679  2254 0 0 0
"AAD" 18679   680 0 0 0
"AAD" 18679  1815 0 0 0
"AAD" 18679  3033 0 0 0
"AAD" 18679    74 0 0 0
"AAD" 18679  1816 0 0 0
"AAD" 18679    88 0 0 0
"AAD" 18679  2159 0 0 0
"AAD" 18679   261 0 0 0
"AAD" 18679  2114 0 0 0
"AAD" 18679  1899 0 0 0
"AAD" 18679   500 0 0 0
"AAD" 18679  1728 0 0 0
"AAD" 18679  2403 0 0 0
"AAD" 18679   986 0 0 0
"AAD" 18679  1115 0 0 0
"AAD" 18679   690 0 0 0
"AAD" 18679 33270 0 0 0
"AAD" 18679   218 0 0 0
"AAD" 18679  1707 0 0 0
"AAD" 18679    75 0 0 0
"AAD" 18679  1632 0 0 0
"AAD" 18679  2530 0 0 0
"AAD" 18679   460 0 0 0
"AAD" 18679  1331 0 0 0
"AAD" 18679   436 0 0 0
"AAD" 18679  2244 0 0 0
"AAD" 18679    21 0 0 0
"AAD" 18679  1429 0 0 0
"AAD" 18679  2306 0 0 0
"AAD" 18679  6747 0 0 0
"AAD" 18679  2258 0 0 0
"AAD" 18679  2416 0 0 0
"AAD" 18679  1818 0 0 0
"AAD" 18679   292 0 0 0
"AAD" 18679  2048 0 0 0
"AAD" 18679 16296 0 0 0
"AAD" 18679 51447 0 0 0
"AAD" 18679 11891 0 0 0
"AAD" 18679    76 0 0 0
"AAD" 18679  2697 0 0 0
"AAD" 18679   141 0 0 0
"AAD" 18679   213 0 0 0
"AAD" 18679  1604 0 0 0
"AAD" 18679   180 0 0 0
"AAD" 18679  1996 0 0 0
"AAD" 18679 11154 0 0 0
"AAD" 18679  1323 0 0 0
"AAD" 18679   638 0 0 0
"AAD" 18679    18 0 0 0
"AAD" 18679  1323 0 0 0
end
format %tdD_m_Y local_Day
                      ------------------ copy up to and including the previous line ------------------

                      Listed 100 out of 29318181 observations
                      Use the count() option to list more

                      . dataex Security local_Day Volume Pre_or_Post_Colo PriceSensitive3 Flag_day_before_ann_1 if PriceSensitive3==1

                      ----------------------- copy starting from the next line -----------------------
                      Code:
                      * Example generated by -dataex-. For more info, type help dataex
clear
input str6 Security long local_Day double(Volume Pre_or_Post_Colo) float PriceSensitive3 byte Flag_day_before_ann_1
"AAD" 18694  3471 0 1 0
"AAD" 18694   581 0 1 0
"AAD" 18694   255 0 1 0
"AAD" 18694  1166 0 1 0
"AAD" 18694   307 0 1 0
"AAD" 18694   544 0 1 0
"AAD" 18694  7500 0 1 0
"AAD" 18694  3761 0 1 0
"AAD" 18694   964 0 1 0
"AAD" 18694     2 0 1 0
"AAD" 18694  1116 0 1 0
"AAD" 18694   354 0 1 0
"AAD" 18694   126 0 1 0
"AAD" 18694   530 0 1 0
"AAD" 18694    76 0 1 0
"AAD" 18694  1174 0 1 0
"AAD" 18694   549 0 1 0
"AAD" 18694  1228 0 1 0
"AAD" 18694   688 0 1 0
"AAD" 18694  1180 0 1 0
"AAD" 18694  2000 0 1 0
"AAD" 18694  1535 0 1 0
"AAD" 18694   214 0 1 0
"AAD" 18694   504 0 1 0
"AAD" 18694   125 0 1 0
"AAD" 18694   952 0 1 0
"AAD" 18694   504 0 1 0
"AAD" 18694   588 0 1 0
"AAD" 18694   572 0 1 0
"AAD" 18694     2 0 1 0
"AAD" 18694   461 0 1 0
"AAD" 18694   216 0 1 0
"AAD" 18694   579 0 1 0
"AAD" 18694  8142 0 1 0
"AAD" 18694  1675 0 1 0
"AAD" 18694   547 0 1 0
"AAD" 18694   530 0 1 0
"AAD" 18694   167 0 1 0
"AAD" 18694   401 0 1 0
"AAD" 18694  1043 0 1 0
"AAD" 18694  6420 0 1 0
"AAD" 18694  2380 0 1 0
"AAD" 18694   578 0 1 0
"AAD" 18694  5122 0 1 0
"AAD" 18694  1362 0 1 0
"AAD" 18694   670 0 1 0
"AAD" 18694   409 0 1 0
"AAD" 18694 11384 0 1 0
"AAD" 18694   130 0 1 0
"AAD" 18694  5256 0 1 0
"AAD" 18694  2300 0 1 0
"AAD" 18694   474 0 1 0
"AAD" 18694   122 0 1 0
"AAD" 18694     2 0 1 0
"AAD" 18694   893 0 1 0
"AAD" 18694  1000 0 1 0
"AAD" 18694   744 0 1 0
"AAD" 18694   470 0 1 0
"AAD" 18694   664 0 1 0
"AAD" 18694    94 0 1 0
"AAD" 18694   276 0 1 0
"AAD" 18694    97 0 1 0
"AAD" 18694     1 0 1 0
"AAD" 18694   335 0 1 0
"AAD" 18694    39 0 1 0
"AAD" 18694 14927 0 1 0
"AAD" 18694   548 0 1 0
"AAD" 18694  1769 0 1 0
"AAD" 18694   257 0 1 0
"AAD" 18694   446 0 1 0
"AAD" 18694     1 0 1 0
"AAD" 18694  7261 0 1 0
"AAD" 18694  2843 0 1 0
"AAD" 18694 20647 0 1 0
"AAD" 18694  2466 0 1 0
"AAD" 18694   476 0 1 0
"AAD" 18694   143 0 1 0
"AAD" 18694   577 0 1 0
"AAD" 18694   535 0 1 0
"AAD" 18694   932 0 1 0
"AAD" 18694   355 0 1 0
"AAD" 18694  1461 0 1 0
"AAD" 18694   147 0 1 0
"AAD" 18694  9468 0 1 0
"AAD" 18694  3271 0 1 0
"AAD" 18694     3 0 1 0
"AAD" 18694   566 0 1 0
"AAD" 18694   169 0 1 0
"AAD" 18694     1 0 1 0
"AAD" 18694   699 0 1 0
"AAD" 18694   345 0 1 0
"AAD" 18694   345 0 1 0
"AAD" 18694   506 0 1 0
"AAD" 18694     3 0 1 0
"AAD" 18694   313 0 1 0
"AAD" 18694   680 0 1 0
"AAD" 18694   405 0 1 0
"AAD" 18694    70 0 1 0
"AAD" 18694     3 0 1 0
"AAD" 18694   541 0 1 0
end
format %tdD_m_Y local_Day
                      ------------------ copy up to and including the previous line ------------------

                      Listed 100 out of 29318181 observations
                      Use the count() option to list more

                      . dataex Security local_Day Volume Pre_or_Post_Colo PriceSensitive3 Flag_day_before_ann_1 if PriceSensitive3==2

                      ----------------------- copy starting from the next line -----------------------
                      Code:
                      * Example generated by -dataex-. For more info, type help dataex
clear
input str6 Security long local_Day double(Volume Pre_or_Post_Colo) float PriceSensitive3 byte Flag_day_before_ann_1
"AAD" 18686   6649 0 2 1
"AAD" 18686   2348 0 2 1
"AAD" 18686   1155 0 2 1
"AAD" 18686   1000 0 2 1
"AAD" 18686    493 0 2 1
"AAD" 18686    249 0 2 1
"AAD" 18686     93 0 2 1
"AAD" 18686    231 0 2 1
"AAD" 18686    154 0 2 1
"AAD" 18686   1982 0 2 1
"AAD" 18686   2078 0 2 1
"AAD" 18686   2238 0 2 1
"AAD" 18686   1316 0 2 1
"AAD" 18686    351 0 2 1
"AAD" 18686    172 0 2 1
"AAD" 18686  14240 0 2 1
"AAD" 18686   4227 0 2 1
"AAD" 18686  16124 0 2 1
"AAD" 18686   1689 0 2 1
"AAD" 18686    115 0 2 1
"AAD" 18686    501 0 2 1
"AAD" 18686   4347 0 2 1
"AAD" 18686   4711 0 2 1
"AAD" 18686    464 0 2 1
"AAD" 18686    158 0 2 1
"AAD" 18686     78 0 2 1
"AAD" 18686    649 0 2 1
"AAD" 18686  18107 0 2 1
"AAD" 18686   8189 0 2 1
"AAD" 18686   2883 0 2 1
"AAD" 18686   2934 0 2 1
"AAD" 18686   7314 0 2 1
"AAD" 18686    198 0 2 1
"AAD" 18686   1239 0 2 1
"AAD" 18686    141 0 2 1
"AAD" 18686     70 0 2 1
"AAD" 18686    270 0 2 1
"AAD" 18686  33383 0 2 1
"AAD" 18686  12861 0 2 1
"AAD" 18686   8720 0 2 1
"AAD" 18686   3638 0 2 1
"AAD" 18686    458 0 2 1
"AAD" 18686    886 0 2 1
"AAD" 18686    897 0 2 1
"AAD" 18686    293 0 2 1
"AAD" 18686     98 0 2 1
"AAD" 18686   8654 0 2 1
"AAD" 18686   7897 0 2 1
"AAD" 18686    931 0 2 1
"AAD" 18686     70 0 2 1
"AAD" 18686   9852 0 2 1
"AAD" 18686   5459 0 2 1
"AAD" 18686    501 0 2 1
"AAD" 18686    115 0 2 1
"AAD" 18686  14205 0 2 1
"AAD" 18686  20447 0 2 1
"AAD" 18686   2632 0 2 1
"AAD" 18686  15707 0 2 1
"AAD" 18686  14805 0 2 1
"AAD" 18686     53 0 2 1
"AAD" 18686   1666 0 2 1
"AAD" 18686   4386 0 2 1
"AAD" 18686  11403 0 2 1
"AAD" 18686      1 0 2 1
"AAD" 18686    378 0 2 1
"AAD" 18686   7987 0 2 1
"AAD" 18686   3392 0 2 1
"AAD" 18686     78 0 2 1
"AAD" 18686   1510 0 2 1
"AAD" 18686   1114 0 2 1
"AAD" 18686  18875 0 2 1
"AAD" 18686  11580 0 2 1
"AAD" 18686  18726 0 2 1
"AAD" 18686   4628 0 2 1
"AAD" 18686   6481 0 2 1
"AAD" 18686    589 0 2 1
"AAD" 18686     21 0 2 1
"AAD" 18686   3847 0 2 1
"AAD" 18686    161 0 2 1
"AAD" 18686      8 0 2 1
"AAD" 18686      4 0 2 1
"AAD" 18686    185 0 2 1
"AAD" 18686    908 0 2 1
"AAD" 18686   7205 0 2 1
"AAD" 18686    288 0 2 1
"AAD" 18686    108 0 2 1
"AAD" 18686   9672 0 2 1
"AAD" 18686   4083 0 2 1
"AAD" 18686    940 0 2 1
"AAD" 18686  12668 0 2 1
"AAD" 18686  13659 0 2 1
"AAD" 18686 162030 0 2 1
"AAD" 18686   4594 0 2 1
"AAD" 18686     73 0 2 1
"AAD" 18686  26936 0 2 1
"AAD" 18686  16392 0 2 1
"AAD" 18686  15984 0 2 1
"AAD" 18686  14133 0 2 1
"AAD" 18686   5786 0 2 1
"AAD" 18686   9566 0 2 1
end
format %tdD_m_Y local_Day
                      ------------------ copy up to and including the previous line ------------------

                      Listed 100 out of 29318181 observations
                      Use the count() option to list more

                      . dataex Security local_Day Volume Pre_or_Post_Colo PriceSensitive3 Flag_day_before_ann_1 if Flag_day_before_ann_1==1

                      ----------------------- copy starting from the next line -----------------------
                      Code:
                      * Example generated by -dataex-. For more info, type help dataex
clear
input str6 Security long local_Day double(Volume Pre_or_Post_Colo) float PriceSensitive3 byte Flag_day_before_ann_1
"AAD" 18683  1747 0 0 1
"AAD" 18683  1156 0 0 1
"AAD" 18683  2000 0 0 1
"AAD" 18683   478 0 0 1
"AAD" 18683    86 0 0 1
"AAD" 18683   480 0 0 1
"AAD" 18683  1664 0 0 1
"AAD" 18683   209 0 0 1
"AAD" 18683  3842 0 0 1
"AAD" 18683   398 0 0 1
"AAD" 18683  1413 0 0 1
"AAD" 18683   111 0 0 1
"AAD" 18683  1425 0 0 1
"AAD" 18683   960 0 0 1
"AAD" 18683  1240 0 0 1
"AAD" 18683   109 0 0 1
"AAD" 18683    70 0 0 1
"AAD" 18683    32 0 0 1
"AAD" 18683  2057 0 0 1
"AAD" 18683   115 0 0 1
"AAD" 18683  1209 0 0 1
"AAD" 18683    44 0 0 1
"AAD" 18683   427 0 0 1
"AAD" 18683   417 0 0 1
"AAD" 18683  1116 0 0 1
"AAD" 18683    63 0 0 1
"AAD" 18683  1567 0 0 1
"AAD" 18683  4185 0 0 1
"AAD" 18683   107 0 0 1
"AAD" 18683   551 0 0 1
"AAD" 18683  1053 0 0 1
"AAD" 18683   115 0 0 1
"AAD" 18683    71 0 0 1
"AAD" 18683  1842 0 0 1
"AAD" 18683   103 0 0 1
"AAD" 18683  1642 0 0 1
"AAD" 18683  1090 0 0 1
"AAD" 18683  1944 0 0 1
"AAD" 18683   122 0 0 1
"AAD" 18683     1 0 0 1
"AAD" 18683    70 0 0 1
"AAD" 18683  1327 0 0 1
"AAD" 18683     7 0 0 1
"AAD" 18683  3627 0 0 1
"AAD" 18683    70 0 0 1
"AAD" 18683   347 0 0 1
"AAD" 18683  4048 0 0 1
"AAD" 18683 11955 0 0 1
"AAD" 18683  2270 0 0 1
"AAD" 18683   648 0 0 1
"AAD" 18683 43621 0 0 1
"AAD" 18683  2706 0 0 1
"AAD" 18683  8751 0 0 1
"AAD" 18683   391 0 0 1
"AAD" 18683   997 0 0 1
"AAD" 18683   162 0 0 1
"AAD" 18683  1493 0 0 1
"AAD" 18683  4812 0 0 1
"AAD" 18683  5000 0 0 1
"AAD" 18683   860 0 0 1
"AAD" 18683   888 0 0 1
"AAD" 18683  3463 0 0 1
"AAD" 18683  1500 0 0 1
"AAD" 18683  1605 0 0 1
"AAD" 18683   816 0 0 1
"AAD" 18683  2373 0 0 1
"AAD" 18683  1009 0 0 1
"AAD" 18683   435 0 0 1
"AAD" 18683   344 0 0 1
"AAD" 18683   661 0 0 1
"AAD" 18683  3500 0 0 1
"AAD" 18683  3946 0 0 1
"AAD" 18683    13 0 0 1
"AAD" 18683   399 0 0 1
"AAD" 18683    71 0 0 1
"AAD" 18683     1 0 0 1
"AAD" 18683  1890 0 0 1
"AAD" 18683 63235 0 0 1
"AAD" 18683 43803 0 0 1
"AAD" 18683 29714 0 0 1
"AAD" 18683 18186 0 0 1
"AAD" 18683  8422 0 0 1
"AAD" 18683    50 0 0 1
"AAD" 18683    65 0 0 1
"AAD" 18683  1219 0 0 1
"AAD" 18683   701 0 0 1
"AAD" 18683  4846 0 0 1
"AAD" 18683    37 0 0 1
"AAD" 18683    81 0 0 1
"AAD" 18683    41 0 0 1
"AAD" 18683   142 0 0 1
"AAD" 18683  5531 0 0 1
"AAD" 18683  4647 0 0 1
"AAD" 18683  2370 0 0 1
"AAD" 18683 28212 0 0 1
"AAD" 18683 34655 0 0 1
"AAD" 18683  1366 0 0 1
"AAD" 18683  3810 0 0 1
"AAD" 18683   569 0 0 1
"AAD" 18683    37 0 0 1
end
format %tdD_m_Y local_Day
                      Last edited by Imtiaz Bhayat; 01 Dec 2022, 04:04.

                      Comment

                      • #12
                        You can do
                        Code:
                        dataex, count(600)
                        if you want! Anyways, the stock announcement seems like the policy, no? Which tells me, that your unit of analysis is, in fact, the security variable, right?

                        I should say, the reason why I'm bothering to ask any of this, is because knowing one's unit of analysis and, relatedly, time period, is quintessential to your quasi-experimental setup. So now that we know the unit is the stock/security, my next question is what's your time period? I see that you have a local_day variable, but this can't be your real time variable. Why?
                        Code:
                        * Example generated by -dataex-. For more info, type help dataex
clear
input str6 Security long local_Day double(Volume Pre_or_Post_Colo) float PriceSensitive3 byte Flag_day_before_ann_1
"AAD" 18683  1747 0 0 1
"AAD" 18683  1156 0 0 1
"AAD" 18683  2000 0 0 1
"AAD" 18683   478 0 0 1
"AAD" 18683    86 0 0 1
"AAD" 18683   480 0 0 1
"AAD" 18683  1664 0 0 1
"AAD" 18683   209 0 0 1
"AAD" 18683  3842 0 0 1
"AAD" 18683   398 0 0 1
"AAD" 18683  1413 0 0 1
"AAD" 18683   111 0 0 1
"AAD" 18683  1425 0 0 1
"AAD" 18683   960 0 0 1
"AAD" 18683  1240 0 0 1
"AAD" 18683   109 0 0 1
"AAD" 18683    70 0 0 1
"AAD" 18683    32 0 0 1
"AAD" 18683  2057 0 0 1
"AAD" 18683   115 0 0 1
"AAD" 18683  1209 0 0 1
"AAD" 18683    44 0 0 1
"AAD" 18683   427 0 0 1
"AAD" 18683   417 0 0 1
"AAD" 18683  1116 0 0 1
"AAD" 18683    63 0 0 1
"AAD" 18683  1567 0 0 1
"AAD" 18683  4185 0 0 1
"AAD" 18683   107 0 0 1
"AAD" 18683   551 0 0 1
"AAD" 18683  1053 0 0 1
"AAD" 18683   115 0 0 1
"AAD" 18683    71 0 0 1
"AAD" 18683  1842 0 0 1
"AAD" 18683   103 0 0 1
"AAD" 18683  1642 0 0 1
"AAD" 18683  1090 0 0 1
"AAD" 18683  1944 0 0 1
"AAD" 18683   122 0 0 1
"AAD" 18683     1 0 0 1
"AAD" 18683    70 0 0 1
"AAD" 18683  1327 0 0 1
"AAD" 18683     7 0 0 1
"AAD" 18683  3627 0 0 1
"AAD" 18683    70 0 0 1
"AAD" 18683   347 0 0 1
"AAD" 18683  4048 0 0 1
"AAD" 18683 11955 0 0 1
"AAD" 18683  2270 0 0 1
"AAD" 18683   648 0 0 1
"AAD" 18683 43621 0 0 1
"AAD" 18683  2706 0 0 1
"AAD" 18683  8751 0 0 1
"AAD" 18683   391 0 0 1
"AAD" 18683   997 0 0 1
"AAD" 18683   162 0 0 1
"AAD" 18683  1493 0 0 1
"AAD" 18683  4812 0 0 1
"AAD" 18683  5000 0 0 1
"AAD" 18683   860 0 0 1
"AAD" 18683   888 0 0 1
"AAD" 18683  3463 0 0 1
"AAD" 18683  1500 0 0 1
"AAD" 18683  1605 0 0 1
"AAD" 18683   816 0 0 1
"AAD" 18683  2373 0 0 1
"AAD" 18683  1009 0 0 1
"AAD" 18683   435 0 0 1
"AAD" 18683   344 0 0 1
"AAD" 18683   661 0 0 1
"AAD" 18683  3500 0 0 1
"AAD" 18683  3946 0 0 1
"AAD" 18683    13 0 0 1
"AAD" 18683   399 0 0 1
"AAD" 18683    71 0 0 1
"AAD" 18683     1 0 0 1
"AAD" 18683  1890 0 0 1
"AAD" 18683 63235 0 0 1
"AAD" 18683 43803 0 0 1
"AAD" 18683 29714 0 0 1
"AAD" 18683 18186 0 0 1
"AAD" 18683  8422 0 0 1
"AAD" 18683    50 0 0 1
"AAD" 18683    65 0 0 1
"AAD" 18683  1219 0 0 1
"AAD" 18683   701 0 0 1
"AAD" 18683  4846 0 0 1
"AAD" 18683    37 0 0 1
"AAD" 18683    81 0 0 1
"AAD" 18683    41 0 0 1
"AAD" 18683   142 0 0 1
"AAD" 18683  5531 0 0 1
"AAD" 18683  4647 0 0 1
"AAD" 18683  2370 0 0 1
"AAD" 18683 28212 0 0 1
"AAD" 18683 34655 0 0 1
"AAD" 18683  1366 0 0 1
"AAD" 18683  3810 0 0 1
"AAD" 18683   569 0 0 1
"AAD" 18683    37 0 0 1
end
format %tdD_m_Y local_Day

egen id = group(Sec), label(Sec)

xtset id local_Day

br
                        returns that there are repeated time values within the panel. That is, your AAD stock can't have a volume of 1747 AND 2000 on the same day. It would be like saying Mumbai can have a population of20,961,000 and 30,000,000 in the same year- just not possible, how would Stata know which value to use? This suggests to me that you have data at the daily, minute by minute level or some smaller aggregation. Again, the reason why I'm saying all this is because you need to be able to xtset your data before you can continue. Otherwise, you will go no further. See how
                        Code:
                        clear *

u "http://fmwww.bc.edu/repec/bocode/s/scul_basque.dta", clear

loc int_time = 1975

keep id-gdp

xtset

l `r(panelvar)' `r(timevar)' if inrange(year,1970,1971)


cls


qui xtset
local lbl: value label `r(panelvar)'

loc unit ="Basque Country (Pais Vasco)":`lbl'


g treat = cond(`r(panelvar)'==`unit' & `r(timevar)' >= `int_time',1,0)
                        in this example, each autonomous community has one year indexed to it? Madrid and Valencia can't have two GDPs a year, nor can they have two populations or five different levels of domestic investment, each unit (region/state) has one year indexed to it, because there are no repeating time values within panels, each panel is uniquely identified by the year. At present, DD, or indeed any analysis isn't feasible, since you need a tidy panel dataset. So in your case, each stock must be id'D not just by the stock day, but the stock... day... minute, or something similar?

                        Comment

                        • #13
                          Thanks Jared. The unit of analysis is not the stock. Often in stock market analysis the work is done at a consolidated market level rather than at a stock level. This is also the case in my work. I would never be looking at BHP and comparing it to Rio Tinto, for instance. Rather, I would be comparing the entire basket of 200 stocks and looking at the movement in one year compared to the next. But when creating a unique identifier I think I need to use the stock name.

                          With respect to your points around the time period and your reference to xtset (which I read about, thankyou). I think I understand your points. And they are:
                          I need a unique identifier (as per your original message) and reflecting on your population example. For my data set, as it is a minute by minute data set, I would need to create a new variable that consists of the stock name, the date and the minute (as per your very wise suggestion). This new field would be unique.

                          Is it correct that my next steps are to:
                          - Create the new unique field?
                          - Run xtset command over my dataset using the date field as the time variable (dd/mm/yyyy)?

                          Comment

                          • #14
                            Hi Jared - reading more about the various work required to set up a data set for a DiD, and your notes above, my data is set up in 'long', for a DiD does this matter or should I set it up as 'wide? If I set it up as wide based on the security (stock), that would provide I unique row per security.

                            Comment

                            • #15
                              For 99% of your Stata needs, you'll always want a long panel dataset. One column per variable, one row per time period, with one column for your ID, and another for your time variable. So yes indeed, you're thinking about it the right way. If I were doing this, I'd do

                              Code:
                              egen id = group(Security), label(Security)
xtset id [timevariable]
                              you say you have minute by minute data, as i figured. Stata can work with these, but you'll need to format them properly. Assuming you have observations for every minute, you have 1440 observations per Security, right?

                              Comment

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