Hi there,
I currently have data for a large pool of individuals across four years (2015/16/17/18).
I appended four annual datasets from the 'Diary of Consumer Payment Choice' which contain transactional level data for approx. 2000-3000 individuals who report their transaction behaviour across 4 diary days (diary_day == 0, diary_day == 1, diary_day == 2, diary_day == 3), per year.
Individuals report their transactions on each day of the diary, and can report as many transactions as they wish each day.
I created a "date" variable prior to appending the datasets in order to distinguish between the years of the diaries.
When I input "xtset id date, yearly", I get the "repeated time values within panel" error.
I think this is because my id and date variables do not uniquely identify individuals in the sample, as many respondents report multiple transactions per diary_day.
I read through other posts and I now know that "xtset id" is a possibility if I am not looking to use time-series operators such as lags and leads. Whilst these would be a nice option, I am not sure how critical they will be for my purposes. I am looking to estimate the effect of using certain payment instruments (such as contactless debit cards) on spending amounts, controlling for unobserved heterogeneity. My intention is to use fixed effects (and potentially pooled OLS and random effects). I am still in the early stages of my research.
(I am using Stata/MP 15.1)
Below I have posted the data:
If "xtset id" is not appropriate here, could you please advise how to proceed? I am also not sure whether I could use "xtset id" and then simply include year dummies in my regression.
Thank you for your help in advance.
Jack
I currently have data for a large pool of individuals across four years (2015/16/17/18).
I appended four annual datasets from the 'Diary of Consumer Payment Choice' which contain transactional level data for approx. 2000-3000 individuals who report their transaction behaviour across 4 diary days (diary_day == 0, diary_day == 1, diary_day == 2, diary_day == 3), per year.
Individuals report their transactions on each day of the diary, and can report as many transactions as they wish each day.
I created a "date" variable prior to appending the datasets in order to distinguish between the years of the diaries.
When I input "xtset id date, yearly", I get the "repeated time values within panel" error.
I think this is because my id and date variables do not uniquely identify individuals in the sample, as many respondents report multiple transactions per diary_day.
I read through other posts and I now know that "xtset id" is a possibility if I am not looking to use time-series operators such as lags and leads. Whilst these would be a nice option, I am not sure how critical they will be for my purposes. I am looking to estimate the effect of using certain payment instruments (such as contactless debit cards) on spending amounts, controlling for unobserved heterogeneity. My intention is to use fixed effects (and potentially pooled OLS and random effects). I am still in the early stages of my research.
(I am using Stata/MP 15.1)
Below I have posted the data:
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input long id float(date diary_day tran) double amnt 100001 2016 0 2 1000 100001 2016 0 . . 100001 2016 0 1 5 100001 2016 1 . . 100001 2016 2 3 127 100001 2016 2 2 820 100001 2016 2 1 30.5 100001 2016 2 . . 100001 2016 2 4 30.400000000000002 100001 2016 3 1 820 100001 2016 3 . . 100001 2017 0 . . 100001 2017 1 1 127 100001 2017 2 1 40 100001 2017 3 1 35 100001 2018 0 . . 100001 2018 1 . . 100001 2018 2 1 10 100001 2018 2 2 89.23 100001 2018 3 . . 100002 2017 0 . . 100002 2017 1 1 25.35 100002 2017 2 1 120 100002 2017 3 1 6.140000000000001 100003 2016 0 1 1623 100003 2016 0 . . 100003 2016 1 . . 100003 2016 1 3 500 100003 2016 1 1 20 100003 2016 1 6 20 100003 2016 1 5 150 100003 2016 1 2 2 100003 2016 1 4 34.15 100003 2016 1 7 25 100003 2016 1 8 20 100003 2016 2 . . 100003 2016 2 2 17.5 100003 2016 2 1 61 100003 2016 3 . . 100003 2016 3 1 2 100003 2017 0 . . 100003 2017 1 2 7.45 100003 2017 1 1 12.99 100003 2017 1 3 15 100003 2017 2 . . 100003 2017 3 2 19.72 100003 2017 3 1 93.97 100003 2017 3 4 1376.33 100003 2017 3 3 23.89 100003 2018 0 . . 100003 2018 1 5 3 100003 2018 1 2 19.150000000000002 100003 2018 1 6 40 100003 2018 1 8 20 100003 2018 1 3 107.92 100003 2018 1 1 13.71 100003 2018 1 7 6 100003 2018 1 4 28 100003 2018 2 1 94.2 100003 2018 2 3 41.51 100003 2018 2 2 22.87 100003 2018 3 2 1696.1000000000001 100004 2017 0 . . 100004 2017 1 1 3.48 100004 2017 2 3 579 100004 2017 2 4 505 100004 2017 2 1 597 100004 2017 3 2 74.84 100004 2017 3 4 389.74 100004 2017 3 3 92.01 100004 2017 3 1 92.01 100004 2017 3 5 389.73 100004 2018 0 . . 100004 2018 1 1 123 100004 2018 1 2 123 100004 2018 2 1 12 100004 2018 2 2 7 100004 2018 3 1 5 100004 2018 3 2 40 100005 2015 0 . . 100005 2015 1 1 100.41 100005 2015 1 . . 100005 2015 2 1 35.81 100005 2015 2 . . 100005 2015 3 1 6.53 100005 2015 3 3 14 100005 2015 3 2 3 100005 2015 3 4 37 100005 2015 3 . . 100005 2016 0 . . 100005 2016 1 1 516.5 100005 2016 1 . . 100005 2016 2 . . 100005 2016 3 . . 100007 2015 0 . . 100007 2015 1 1 5 100007 2015 1 . . 100007 2015 2 . . 100007 2015 2 1 30 100007 2015 3 . . end format %ty date
Thank you for your help in advance.
Jack
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