I currently have a data set that appears as follows
mnbr uact_id hiredate termdate
9 3709 19510101 20000915
20 9409 20001001 20080601
33 25646 19990201 20000731
mnbr represents the member number of a given worker in a labor union. uact_id is the shop they were working for and hiredate and termdate (given yyyymmdd) represent the given dates they were at the shop/uact_id. I am currently trying to use the expand command in Stata to create a panel such that there is one observation per year for each member number (mnbr) between the indicators of hiredate and termdate.
i.e. it should ideally look like
mnbr uact_id year
9 3709 1951
9 3709 1952
9 3709 1953
9 3709 1954
etc. for each member number for each year.
Assuming arbitrarily that the dates are strings, we can go
gen year = real(substr(hiredate, 1, 4))
gen duration = real(substr(termdate, 1, 4)) - year + 1
expand duration
bysort mnbr : replace year = year[_n-1] + 1 if _n > 1
If the dates are numeric, specifically integers, then the first two lines could be
gen year = floor(hiredate/10000)
gen duration = floor(termdate/10000) - year + 1
The replace step is discussed within
How can I replace missing values with previous or following nonmissing values or within sequences?
Related
Datastructure: I use panel data in which an observation represents a certain individual in a given year (2015-2021). Only observations are included of individuals who are between the 15 and 25 years old. There are 2857 observations of 1373 individuals in total.
Goal: The goal is to investigate the effect of a policy change in 2018. In doing so, I designed a quasi-experimental design in which there are two controlgroups and a treatmentgroup defined in terms of their age:
Controlgroup A: individuals 15-17 years old
Treatmentgroup: individuals 18-22 years old
Controlgroup B: individuals 23-25 years old
Dividing individuals into treatment and controlgroups based on varying chance:
due to methodological reasons, individuals selected in a controlgroup may not become part of the treatment group (due to aging over time) and vice versa. Therefore I am confronted with the question how to select the right individuals (given their age and the year) for the treatment and controlgroups.
To ensure that every year has observations of individuals in all ages, I came up with the following design (see picture).
There are 17 theoretically possible individuals in my data (vertical as in the picture) who age over 7 years (2015-2021). I would like to sample the individuals into the treatment and controlgroups based on the chances mentioned in the table beneath to ensure all ages are represented in all years.
Programming
I constructed a variable (1-17) indicating what number an individual represents (like the vertical numbers in the table above)
gen individualnumber=(age-year)+2007
I constructed three variables indicating the chances of being in controlgroup A, B or treatment in the following way:
gen Chanceofbeingcontrol_1517=0
replace Chanceofbeingcontrol_1517=1 if individualnumber==1 | individualnumber==2 | individualnumber==3
replace Chanceofbeingcontrol_1517=0.75 if individualnumber==4
replace Chanceofbeingcontrol_1517=0.60 if individualnumber==5
replace Chanceofbeingcontrol_1517=0.50 if individualnumber==6
replace Chanceofbeingcontrol_1517=0.43 if individualnumber==7
replace Chanceofbeingcontrol_1517=0.29 if individualnumber==8
replace Chanceofbeingcontrol_1517=0.14 if individualnumber==9
gen Chanceofbeingcontrol_2325=0
replace Chanceofbeingcontrol_2325=1 if individualnumber==15 | individualnumber==16 | individualnumber==17
replace Chanceofbeingcontrol_2325=0.75 if individualnumber==14
replace Chanceofbeingcontrol_2325=0.60 if individualnumber==13
replace Chanceofbeingcontrol_2325=0.50 if individualnumber==12
replace Chanceofbeingcontrol_2325=0.43 if individualnumber==11
replace Chanceofbeingcontrol_2325=0.29 if individualnumber==10
replace Chanceofbeingcontrol_2325=0.14 if individualnumber==9
gen Chanceofbeingtreated=1-(Chanceofbeingcontrol_1517+Chanceofbeingcontrol_2325)
After that I wanted to construct the samples...
splitsample, generate(treatedornot) split(Chanceofbeingcontrol_1517 Chanceofbeingtreated Chanceofbeingcontrol_2325) cluster(individualnumber) rround show
...but I received an error since only a numlist might be used in the split(numlist) subcommand.
Question: How to construct the samples or overcome this error in an efficient way?
Example: An individuals (number 7 in the table) who is 15 years old in 2015 (controlgroup 1 age), will be 18 years old in 2018 (which is the treatment age). But this individual may not be part of both the treatment and controlgroup and should therefore be a member of one of the two. Therefore I want to draw three random samples among all number 7 individuals.
Let's state there are 100 individuals like individual 7 in the table.
Sample 1 is controlgroup A and individual 7 will occur 43 times in this sample
Sample 2 is the treatment group so individual 7 occurs 57 times in this sample
While individual 7 will not occur in sample 3 since this person is never older than 22 during 2015-2021.
What's common for all people who were 9 in 2015, 10 in 2016, 11 in 2017 is that they were born 2006. And all who were 10 in 2015 was born 2005. So instead of a variable individualnumber that can be hard to understand for someone who reads your code, why don't you create a variable called birthyear. That will make it easier to explain your design to your peers.
Regardless of what you call the variable or what the value it contains represent, I would solve it something like this. You will probably need to tweak this code. Provide a replicable subset of your data (see the command dataex) if you want a replicable answer.
* Example generated by -dataex-. For more info, type help dataex
clear
input byte id int year double age
1 2017 15
1 2017 15
2 2017 15
2 2017 15
3 2017 15
3 2017 15
4 2017 15
4 2017 15
5 2015 12
5 2015 12
end
* Create the var that will display the
gen birthyear = year-age
preserve
* Collapse year-person level data to person level so
* that each individual only get one treatment status.
* You must have an individual id number for this
* Get standard deviation to test that data is good and the birthyear
* is identical for each individual across the panel data set
collapse (mean) birthyear (sd) bysd=birthyear, by(id)
* Test that birthyear is same across all indivudals - this is not needed,
* but good data quality assurance test. Then drop the var as it is not needed
assert bysd == 0
drop bysd
* Set seed to make replicable. Replace this seed when you have tested this
* script using a new random seed. For example from here:
* https://www.random.org/integers/?num=1&min=100000&max=999999&col=5&base=10&format=html&rnd=new
set seed 123456
*Generate a random number based on the seed
gen random_draw = runiform()
* For each birthyear, get the rank of the random number divided by the number
* of individuals in each birthyear
sort birthyear random_draw
by birthyear : gen percent_rank = _n/_N
*Initiate treatmen variable
gen tmt_status = .
label define tmt_status 0 "Treated" 1 "ControlA" 2 "ControlB"
*Assign birthyear 2006-2004 that are all the same
replace tmt_status = 1 if birthyear == 2006
replace tmt_status = 1 if birthyear == 2005
replace tmt_status = 1 if birthyear == 2004
*Assign birthyear 2003
replace tmt_status = 0 if birthyear == 2003 & percent_rank <= .25
replace tmt_status = 1 if birthyear == 2003 & percent_rank > .25
*Assign birthyear 2002
replace tmt_status = 0 if birthyear == 2002 & percent_rank <= .40
replace tmt_status = 1 if birthyear == 2002 & percent_rank > .40
*Fill in birthyear 2001-1999
*Assign year 1998
replace tmt_status = 0 if birthyear == 1998 & percent_rank <= .72
replace tmt_status = 1 if birthyear == 1998 & percent_rank > .72 & percent_rank <= .86
replace tmt_status = 2 if birthyear == 1998 & percent_rank > .86
*Fill in birthyear 1997-1990
* Do some tabulates etc to convince yourself the randomization is as expected
* Save tempfile of data to be merged to later
* (Consider saving this as a master data set https://worldbank.github.io/dime-data-handbook/measurement.html#constructing-master-data-sets)
tempfile assignment_results
save `assignment_results'
restore
merge m:1 id using `assignment_results'
This code can be made more concise using loop, but random assignment is so important as I personally always go for clarity over conciseness when doing this.
This is not answering specifically about splitsample, but it addresses what you are trying to do. You will have to decide how you want to do with groups that does not have a size that can be split into the exact ratio you prefer.
I have a question on SAS Programming. It is about conditional sum. But it is very specific for me. Therefore, I want to ask as an example. I have the following dataset:
Group A Quantity
1 10 7
1 8 4
1 7 3
1 10 5
2 11 6
2 13 8
2 9 7
2 13 9
I want to add two more columns to this dataset. The new dataset should be:
Group A Quantity B NewColumn
1 10 7 10 12 (7+5)
1 8 4 10 12
1 7 3 10 12
1 10 5 10 12
2 13 6 13 15 (6+9)
2 10 8 13 15
2 9 7 13 15
2 13 9 13 15
So, the column B should be equal tha maximum value of each group and it is the same for all observations of each group. In this example, Group number 1 has 4 values. They are 10, 8, 7, 10. The maximum among these values is 10. Therefore, the values of the observations of the B column for the first group are all equal to 10. Maximum number for group number 2 is 13. Therefore, the values of the observations of the B column for the second group are all equal to 13.
The column C is more complicated. Its value depends on the all columns. Similiar to B column, it will be the same within group. More detailed, it is the sum of the specific observations of QUANTITIES column. These specific observations should belong to the observations that have the maximum value in each group. In our example, it is 12 for the first group. The reason is, the maximum number of first group is 10. and the quantities belong to 10 are 7 and 5. So, the sum of these is 12. For the second group it is 15. because the maximum value of the second group is 13 and the quantities belong to 13 are 6 and 9. So the sum is 15.
I hope. I can explain it. Many thanks in advance.
You can do this with proc sql:
proc sql;
select t.*, max_a as b,
(select sum(t2.quantity)
from t t2
where t2.group = t.group and t.a = max_a
) as c
from t join
(select group, max(a) as max_a
from t
group by group
) g
on t.group = g.group;
run;
If the data is coming from an underlying database, most databases support window functions which make this easier.
This is untested (I'm away from sas) and will probably have mistakes, but a triple DoW loop should work. One pass to get the max per group, second pass to get the sum, third pass to output the records. Something like:
data want ;
do until(last.group) ;
by group ;
set have ;
B=max(A,B) ;
end ;
do until(last.group) ;
set have ;
by group ;
if A = B then NewColumn = sum(NewColumn, Quantity) ;
end;
do until(last.group);
set have ;
by group;
output ;
end ;
run;
I have the following goal regarding my data structure
group; month; year; next_year
1; February; 2014; 0
1; March; 2006; 0
1; November; 2013; 1
2; January; 2014; 0
3; January; 2004; 0
I do have group, month and year, however the column next_year needs to be generated from the first three: For each observation, I want to check if there is another observation within the same group that has a date-value which falls into the period of next year. If so, I want to set the value of next_year to 1, otherwise to 0 (see example).
I started by converting the date into a format that Stata can interpret - using ym(month, year) - such that I can make comparisons. However, I am not sure how to iterate over all observations within the group in order to determine if that is the case or not.
I would know how to do it in e.g. Java, but I don't for Stata. I suppose I should not start with loops as there are probably some implemented commands for such problems.
If you want to check if there is a following observation within the next 12 months, you can try:
clear
set more off
*----- example data -----
input group str8 month year
1 March 2006
1 March 2013
1 November 2013
1 January 2013
2 January 2014
3 January 2004
end
*----- what you want -----
gen dat = monthly(month + string(year), "MY")
format dat %tm
bysort group (dat): gen next = dat[_n+1] - dat <= 12
list, sepby(group)
Make sure you understand the difference between Nick's code and mine. They work under different assumptions. You can check the differences running both pieces of code with the data I have provided (originally Nick's but with one observation deleted to get the point across; by chance, if you use Nick's data without the modification, the results will be the same).
You are correct in avoiding an explicit loop. This kind of problem is soluble using by:.
I modified your example to have two observations for one group in one year.
clear
input group str8 month year
1 February 2014
1 March 2006
1 March 2013
1 November 2013
2 January 2014
3 January 2004
end
bysort group (year) : gen next_year = year[_n+1] == year + 1
bysort group year (next_year) : replace next_year = next_year[_N]
list, sepby(group)
+------------------------------------+
| group month year next_y~r |
|------------------------------------|
1. | 1 March 2006 0 |
2. | 1 November 2013 1 |
3. | 1 March 2013 1 |
4. | 1 February 2014 0 |
|------------------------------------|
5. | 2 January 2014 0 |
|------------------------------------|
6. | 3 January 2004 0 |
+------------------------------------+
Getting an explicit sort order is essential. Within group, we look ahead to see if the next year is the current year plus 1, assigning 1 if true and 0 if false. That will at most be true for the last observation for a given group and year. If there is more than one observation for each group and year, any occurrence of 1 must be spread to all such observations.
For a tutorial on by:, see Speaking Stata: How to move step by: step.
The assumption here is that you mean in the next calendar year, not in the next 12 months. Making your dates into Stata monthly dates will be needed for most other problems, but doesn't make this one easier. Here is one way to do that in your situation, assuming that month is string and year is numeric:
gen mdate = monthly(month + string(year), "MY")
format mdate %tm
Please help me duplicate a variable under certain conditions? My original dataset looks like this:
week category averageprice
1 1 5
1 2 6
2 1 4
2 2 7
This table says that for each week, there is a unique average price for each category of goods.
I need to create the following variables:
averageprice1 (av. price for category 1)
averageprice2 (av. price for category 2)
such that:
week category averageprice1 averageprice2
1 1 5 6
1 2 5 6
2 1 4 7
2 2 4 7
meaning that for week 1, average price for category 1 stayed at $5, and av. price for cater 2 stayed at 6. Similar logic applies to week 2.
As you could see that the new variables are duplicated depending on a week.
I am still learning Stata. I tried:
bysort week: replace averageprice1=averageprice if categ==1
but it doesn't work as expected.
You are not duplicating observations (meaning here in the Stata sense, i.e. cases or records) here at all, as (1) the number of observations remains the same (2) you are copying certain values, not the contents of observations. Similar comment on "duplicating variables". However, that's just loose use of terminology.
Taking your example very literally
clear
input week category averageprice
1 1 5
1 2 6
2 1 4
2 2 7
end
bysort week (category) : gen averageprice1 = averageprice[1]
by week: gen averageprice2 = averageprice[2]
l
+--------------------------------------------------+
| week category averag~e averag~1 averag~2 |
|--------------------------------------------------|
1. | 1 1 5 5 6 |
2. | 1 2 6 5 6 |
3. | 2 1 4 4 7 |
4. | 2 2 7 4 7 |
+--------------------------------------------------+
This is a standard application of subscripting with by:. Your code didn't work because it did not oblige Stata to look in other observations when that is needed. In fact your use of bysort week did not affect how the code applied at all.
EDIT:
A generalization is
egen averageprice1 = mean(averageprice / (category == 1)), by(week)
egen averageprice2 = mean(averageprice / (category == 2)), by(week)
I am using Stata and I have 6 years of daily returns for stocks that individuals hold in their portfolios. I would like to aggregate the daily returns to monthly portfolio returns. In some instances, the individual may hold more than one stock in the portfolio. I am struggling with writing the code to do this.
For a visual, my data looks like this:
I would like the results to look like this:
Where individual 2's portfolio return for the month of December 1996 is calculated as: 0.3 * 0.0031 + 0.7 * 0.0076 = 0.00625.
I have tried the collapse command such as
collapse Return, by (ID Year Month)
but this does not provide the same return that I calculated out in Excel.
I am able to make a weighted portfolio return for all the days using
bysort ID year month: egen wt_return = stock_weight * monthly_return
But this gives me daily returns. My trouble is then aggregating them into one return for the corresponding month.
As for the specifics, I would like to calculate the monthly portfolio return as the product of 1 + the weighted daily returns. As a last resort, the mean return for the month could work.
You don't show monthly portfolio return for person 2 in 1991. Your initial example data doesn't show stock weights but the desired example
data does. Your variable Monthly Return is not reproducible. You should take time to verify your question is clear when posting.
It's supposed be clear to the public who will read it, not only to you.
I didn't bother checking if your computations are correct but below is what I
understand you want. The procedure is simply to compute a weighted return and then
add them up by person year month groups. (I assume the stock weights apply to stocks on a daily basis, which is what your example data implies.)
clear all
set more off
input ///
perid year month day str3 stockid return stockw
1 1991 1 1 "ABC" .01 1
1 1991 1 2 "ABC" .02 1
1 1991 1 3 "ABC" -.01 1
1 1991 1 31 "ABC" .004 1
1 1996 12 31 "ABC" .002 1
2 1991 1 1 "ABC" .01 .3
2 1991 1 2 "ABC" .02 .3
2 1996 12 31 "ABC" .004 .3
2 1991 1 1 "XYZ" .001 .7
2 1991 1 2 "XYZ" .004 .7
2 1996 12 31 "XYZ" .021 .7
end
* create weighted return
gen returnw = return * stockw
sort perid year month day
list, sepby(perid year month day)
* sum weighted returns by person, year, month
collapse (sum) returnw, by (perid year month)
list, sepby(perid)
If you want collapse to sum, then you must indicate it with the (sum) (although I'm not clear if this is what you want). By default, it computes the mean. Read help collapse thouroughly.