Hi there,
I am currently using StataMP 15.1.
I am trying to generate a new variable for the frequency of reported transactions in a panel dataset that tracks payments which are reported by individuals on each day of a 3-day payment diary, across four years (2015-2018).
Each observation/row is a reported transaction for an individual on one particular diary day for that year. Year variable = date.
I already have the transaction amount, under the variable amnt, but I would like to create a variable that tracks the total number of transactions reported per individual per year ("year" being the whole of the three-day diary).
E.g. In 2016, individual 100001 reported a total of 7 transactions across the three days.
I have a variable called tran which counts the transactions per diary day. So if the value of tran was 3, that implies that that transaction is the 3rd one of that particular day.
I tried the following code:
bysort id date diary_day: egen tran_freq = max(tran)
Unfortunately this does not really help me, but perhaps it is a starting point...?
My end goal is to be able to estimate the effect of certain payment instruments, e.g. contactless payments, on reported transaction frequency.
I will insert the data below:
Thank you in advance for any help.
Jack
I am currently using StataMP 15.1.
I am trying to generate a new variable for the frequency of reported transactions in a panel dataset that tracks payments which are reported by individuals on each day of a 3-day payment diary, across four years (2015-2018).
Each observation/row is a reported transaction for an individual on one particular diary day for that year. Year variable = date.
I already have the transaction amount, under the variable amnt, but I would like to create a variable that tracks the total number of transactions reported per individual per year ("year" being the whole of the three-day diary).
E.g. In 2016, individual 100001 reported a total of 7 transactions across the three days.
I have a variable called tran which counts the transactions per diary day. So if the value of tran was 3, that implies that that transaction is the 3rd one of that particular day.
I tried the following code:
bysort id date diary_day: egen tran_freq = max(tran)
Unfortunately this does not really help me, but perhaps it is a starting point...?
My end goal is to be able to estimate the effect of certain payment instruments, e.g. contactless payments, on reported transaction frequency.
I will insert the data below:
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input long id float(date diary_day tran tran_freq) double amnt int pi 100001 2016 0 2 2 1000 . 100001 2016 0 . 2 . . 100001 2016 0 1 2 5 . 100001 2016 1 . . . . 100001 2016 2 3 4 127 2 100001 2016 2 2 4 820 3 100001 2016 2 1 4 30.5 2 100001 2016 2 . 4 . . 100001 2016 2 4 4 30.400000000000002 2 100001 2016 3 1 1 820 3 100001 2016 3 . 1 . . 100001 2017 0 . . . . 100001 2017 1 1 1 127 3 100001 2017 2 1 1 40 3 100001 2017 3 1 1 35 1 100001 2018 0 . . . . 100001 2018 1 . . . . 100001 2018 2 1 2 10 1 100001 2018 2 2 2 89.23 3 100001 2018 3 . . . . 100002 2017 0 . . . . 100002 2017 1 1 1 25.35 4 100002 2017 2 1 1 120 4 100002 2017 3 1 1 6.140000000000001 1 100003 2016 0 1 1 1623 . 100003 2016 0 . 1 . . 100003 2016 1 . 8 . . 100003 2016 1 3 8 500 10 100003 2016 1 1 8 20 11 100003 2016 1 6 8 20 . 100003 2016 1 5 8 150 . 100003 2016 1 2 8 2 1 100003 2016 1 4 8 34.15 4 100003 2016 1 7 8 25 10 100003 2016 1 8 8 20 . 100003 2016 2 . 2 . . 100003 2016 2 2 2 17.5 10 100003 2016 2 1 2 61 4 100003 2016 3 . 1 . . 100003 2016 3 1 1 2 1 100003 2017 0 . . . . 100003 2017 1 2 3 7.45 4 100003 2017 1 1 3 12.99 4 100003 2017 1 3 3 15 4 100003 2017 2 . . . . 100003 2017 3 2 4 19.72 4 100003 2017 3 1 4 93.97 3 100003 2017 3 4 4 1376.33 . 100003 2017 3 3 4 23.89 4 100003 2018 0 . . . . 100003 2018 1 5 8 3 . 100003 2018 1 2 8 19.150000000000002 4 100003 2018 1 6 8 40 . 100003 2018 1 8 8 20 1 100003 2018 1 3 8 107.92 4 100003 2018 1 1 8 13.71 4 100003 2018 1 7 8 6 1 100003 2018 1 4 8 28 4 100003 2018 2 1 3 94.2 6 100003 2018 2 3 3 41.51 3 100003 2018 2 2 3 22.87 3 100003 2018 3 2 2 1696.1000000000001 . 100004 2017 0 . . . . 100004 2017 1 1 1 3.48 4 100004 2017 2 3 4 579 6 100004 2017 2 4 4 505 . 100004 2017 2 1 4 597 2 100004 2017 3 2 5 74.84 4 100004 2017 3 4 5 389.74 . 100004 2017 3 3 5 92.01 6 100004 2017 3 1 5 92.01 2 100004 2017 3 5 5 389.73 . 100004 2018 0 . . . . 100004 2018 1 1 2 123 2 100004 2018 1 2 2 123 2 100004 2018 2 1 2 12 4 100004 2018 2 2 2 7 4 100004 2018 3 1 2 5 4 100004 2018 3 2 2 40 4 100005 2015 0 . . . . 100005 2015 1 1 1 100.41 3 100005 2015 1 . 1 . . 100005 2015 2 1 1 35.81 3 100005 2015 2 . 1 . . 100005 2015 3 1 4 6.53 3 100005 2015 3 3 4 14 . 100005 2015 3 2 4 3 1 100005 2015 3 4 4 37 3 100005 2015 3 . 4 . . 100005 2016 0 . . . . 100005 2016 1 1 1 516.5 3 100005 2016 1 . 1 . . 100005 2016 2 . . . . 100005 2016 3 . . . . 100007 2015 0 . . . . 100007 2015 1 1 1 5 1 100007 2015 1 . 1 . . 100007 2015 2 . 1 . . 100007 2015 2 1 1 30 1 100007 2015 3 . 2 . . end label values pi pi_l label def pi_l 1 "1 Cash", modify label def pi_l 2 "2 Check", modify label def pi_l 3 "3 Credit card", modify label def pi_l 4 "4 Debit card", modify label def pi_l 6 "6 Bank account number payment", modify label def pi_l 10 "10 PayPal", modify label def pi_l 11 "11 Account-to-account transfer", modify
Jack
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