I am dealing with panel data with a time gap. but not the same time gap.
Year variable has 1980, 1990, 2000, 2010, 2015, and 2020.
As you can see it has a 10 year time gap up to 2010, but five-years between 2010 and 2020.
After setting up for panel data structure in Stata (using xtset command), I wanted to use the time (lag) operator for my main variable interest and outcome variable. However, when I use L. in front of the variable name, Stata tells me no observations.
Isn't it automatically taking the previous time period?
Or do I create manually the lag variables?
What we need to know, but can't see, is exactly what code you used, specifically xtset. But it's possible to guess. Here I fabricate one panel; a structure with more panels doesn't show different problems.
clear
input Y Year
1 1980
2 1990
3 2000
4 2010
5 2015
6 2020
end
gen ID = 42
If you just specify panel and year variables, Stata expects unit spacing, so lag 1 with yearly data means "the previous year". Asking for a lag 1 variable is legal, but all values are missing.
xtset ID Year
gen lag1 = L1.Y
If you specify delta(5) then a lag 1 variable is missing in all but two observations.
xtset ID Year, delta(5)
gen lag5 = L1.Y
If you try delta(10) that won't work (unless you drop 2015).
xtset ID Year, delta(10)
You can also do this:
bysort ID (Year) : gen prev = Y[_n-1]
Bringing your results together
list , sep(0)
+------------------------------------+
| Y Year ID lag1 lag5 prev |
|------------------------------------|
1. | 1 1980 42 . . . |
2. | 2 1990 42 . . 1 |
3. | 3 2000 42 . . 2 |
4. | 4 2010 42 . . 3 |
5. | 5 2015 42 . 4 4 |
6. | 6 2020 42 . 5 5 |
+------------------------------------+
The no observations error message presumably comes from some other command.
Related
This is somewhat of a continuation of a previous post but I am trying to forecast weekly revenues. My program seems to hang on the adfuller test. It has run before and appears stationary via p-value but not consistently. I have added SARIMAX in as well and the code just hangs. If I cancel out I get a message towards the bottom (periodically) that says the problem is unconstrained.
Data:
Week | Week_Start_Date |Amount |year
Week 1 2018-01-01 42920 2018
Week 2 2018-01-08 37772 2018
Week 3 2018-01-15 41076 2018
Week 4 2018-01-22 38431 2018
Week 5 2018-01-29 101676 2018
Code:
x = organic_search.groupby('Week_Start_Date').Amount.sum()
# Augmented Dickey-Fuller test
ad_fuller_result = adfuller(x)
print(f'ADF Statistic: {ad_fuller_result[0]}')
print(f'p-value: {ad_fuller_result[1]}')
# SARIMA Model
plt.figure(2)
best_model = SARIMAX(x, order=(2, 1, 1), seasonal_order=(2, 1, 1, 52)).fit(dis=1)
print(best_model.summary())
best_model.plot_diagnostics(figsize=(15,12))
I am only working with 185 or so rows. I don't understand why code is just hanging. Any optimization suggestions welcome (for adfuller and SARIMAX).
Fixed via passing organic_search['Amount'] instead of organic_search.groupby('Week_Start_Date').Amount.sum()
I'm trying to run a for loop to make a balance table in Stata (comparing the demographics of my dataset with national-level statistics)
For this, I'm prepping my dataset and attempting to calculate the percentages/averages for some key demographics.
preserve
rename unearnedinc_wins95 unearninc_wins95
foreach var of varlist fem age nonwhite hhsize parent employed savings_wins95 debt_wins95 earnedinc_wins95 unearninc_wins95 underfpl2019 { //continuous or binary; to put categorical vars use kwallis test
dis "for variable `var':"
tabstat `var'
summ `var'
local `var'_samplemean=r(mean)
}
clear
set obs 11
gen var=""
gen sample=.
gen F=.
gen pvalue=.
replace var="% Female" if _n==1
replace var="Age" if _n==2
replace var="% Non-white" if _n==3
replace var="HH size" if _n==4
replace var="% Parent" if _n==5
replace var="% Employed" if _n==6
replace var="Savings stock ($)" if _n==7
replace var="Debt stock ($)" if _n==8
replace var="Earned income last mo. ($)" if _n==9
replace var="Unearned income last mo. ($)" if _n==10
replace var="% Under FPL 2019" if _n==11
foreach col of varlist sample {
replace `col'=100*round(`fem_`col'mean', 0.01) if _n==1
replace `col'=round(`age_`col'mean') if _n==2
replace `col'=100*round(`nonwhite_`col'mean', 0.01) if _n==3
replace `col'=round(`hhsize_`col'mean', 0.1) if _n==4
replace `col'=100*round(`parent_`col'mean', 0.01) if _n==5
replace `col'=100*round(`employed_`col'mean', 0.01) if _n==6
replace `col'=round(`savings_wins95_`col'mean') if _n==7
replace `col'=round(`debt_wins95_`col'mean') if _n==8
replace `col'=round(`earnedinc_wins95_`col'mean') if _n==9
replace `col'=round(`unearninc_wins95_`col'mean') if _n==10
replace `col'=100*round(`underfpl2019_`col'mean', 0.01) if _n==11
}
I'm trying to run the following loop, but in the second half of the loop, I keep getting an 'invalid syntax' error. For context, in the first half of the loop (before clearing the dataset), the code stores the average values of the variables as a macro (`var'_samplemean). Can someone help me out and mend this loop?
My sample data:
clear
input byte fem float(age nonwhite) byte(hhsize parent) float employed double(savings_wins95 debt_wins95 earnedinc_wins95 unearninc_wins95) float underfpl2019
1 35 1 6 1 1 0 2500 0 0 0
0 40 0 4 1 1 0 10000 1043 0 0
0 40 0 4 1 1 0 20000 2400 0 0
0 40 0 4 1 1 .24 20000 2000 0 0
0 40 0 4 1 1 10 . 2600 0 0
Thanks!
Thanks for sharing the snippet of data. Apart from the fact the variable unearninc_wins95 has already been renamed in your sample data, the code runs fine for me without returning an error.
That being said, the columns for your F-statistics and p-values are empty once the loop at the bottom of your code completes. As far as I can see there is no local/varlist called sample which you're attempting to call with the line foreach col of varlist sample{. This could be because you haven't included it in your code, in which case please do, or it could be because you haven't created the local/varlist sample, in which case this could well be the source of your error message.
Taking a step back, there are more efficient ways of achieving what I think you're after. For example, you can get (part of) what you want using the package stat2data (if you don't have it installed already, run ssc install stat2data from the command prompt). You can then run the following code:
stat2data fem age nonwhite hhsize parent employed savings_wins95 debt_wins95 earnedinc_wins95 unearninc_wins95 underfpl2019, saving("~/yourstats.dta") stat(count mean)
*which returns:
preserve
use "~/yourstats.dta", clear
. list, sep(11)
+----------------------------+
| _name sN smean |
|----------------------------|
1. | fem 5 .2 |
2. | age 5 39 |
3. | nonwhite 5 .2 |
4. | hhsize 5 4.4 |
5. | parent 5 1 |
6. | employed 5 1 |
7. | savings_wins 5 2.048 |
8. | debt_wins95 4 13125 |
9. | earnedinc_wi 5 1608.6 |
10. | unearninc_wi 5 0 |
11. | underfpl2019 5 0 |
+----------------------------+
restore
This is missing the empty F-statistic and p-value variables you created in your code above, but you can always add them in the same way you have with gen F=. and gen pvalue=.. The presence of these variables though indicates you want to run some tests at some point and then fill the cells with values from them. I'd offer advice on how to do this but it's not obvious to me from your code what you want to test. If you can clarify this I will try and edit this answer to include that.
This doesn't answer your question directly; as others gently point out the question is hard to answer without a reproducible example. But I have several small comments on your code which are better presented in this form.
Assuming that all the variables needed are indeed present in the dataset, I would recommend something more like this:
local myvarlist fem age nonwhite hhsize parent employed savings_wins95 debt_wins95 earnedinc_wins95 unearninc_wins95 underfpl2019
local desc `" "% Female" "Age" "% Non-white" "HH size" "% Parent" "% Employed" "Savings stock ($)" "Debt stock ($)" "Earned income last mo. ($)" "Unearned income last mo. ($)" "% Under FPL 2019" "'
local i = 1
gen variable = ""
gen mean = ""
local i = 1
foreach var of local myvars {
summ `var', meanonly
local this : word `i' of `desc'
replace variable = "`this'" in `i'
if inlist(`i', 1, 3, 5, 6, 11) {
replace mean = strofreal(100 * r(mean), "%2.0f") in `i'
}
else if `i' == 4 {
replace mean = strofreal(r(mean), "%2.1f") in `i'
}
else replace mean = strofreal(r(mean), "%2.0f") in `i'
local ++i
}
This has not been tested.
Points arising include:
Using in is preferable for what you want over testing the observation number with if.
round() is treacherous for rounding to so many decimal places. Most of the time you will get what you want, but occasionally you will get bizarre results arising from the fact that Stata works in binary, like any equivalent program. It is safer to treat rounding as a problem in string manipulation and use display formats as offering precisely what you want.
If the text you want to show is just the variable label for each variable, this code could be simplified further.
The code hints at intent to show other stuff, which is easily done compatibly with this design.
My dataset consists of a number of variables:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float(v1 v2) str11 Date float(v4 v5 v6 v7 v8)
1 2 "15-aug-2016" 1 1 1 1 1
1 2 "07-may-2015" 1 1 1 1 50
1 2 "07-may-2015" 1 1 1 1 88
1 2 "15-aug-2016" 1 1 1 1 29
end
The variable date is a date and time and is formatted as a datetime
generate double date = date(Date,"DMY")
My duplicates are the same for v1-v2-v4-v5-v6-v7 (as in the example), while v8 is different.
I need to delete duplicates based on v1-v2-v4-v5-v6-v7 and keep the one with the smallest date (here 07-may-2015).
I have tried without success:
1.
gsort -date
bysort v1 v2 v4 v5 v6 v7: generate dublet=_n
order dublet date
keep if dublet==1
drop dublet
--> Works for the first 25 rows or so, then keeps the wrong one a couple of times and then the right one again. (Seems to me, that the bysort command removes the sort done by gsort? Any knowing if that's correct?)
bysort v1 v2 v4 v5 v6 v7 (date) : keep if _n == _N
--> Obviously keeps the wrong one, since Date is not -Date.
However, -Date is not an option - Stata writes: - invalid name
You could change your second answer to bysort v1 v2 v4 v5 v6 v7 (date) : keep if _n == 1 and that should give you what you're looking for.
Since in your data example there are duplicate dates (2 observations are May 7th 2015) you will get a random one of the observations with the minimum date.
I have a spark dataFrame that looks like this:
id dates value
1 11 2013-11-15 10
2 11 2013-11-16 15
3 22 2013-11-15 20
4 22 2013-11-16 21
5 22 2013-11-17 3
I wish to retain the value from the previous date per id.
The final result should look like this:
id dates value prev_value
1 11 2013-11-15 10 NA
2 11 2013-11-16 15 10
3 22 2013-11-15 20 NA
4 22 2013-11-16 21 20
5 22 2013-11-17 3 21
The solution from this question would not work for various reasons.
I would appreciate the help!
So after playing with it for a while, here's the workaround that I found:
First of all, here's the example DF
id<-c(11,11,22,22,22)
dates<-as.Date(c('2013-11-15','2013-11-16','2013-11-15','2013-11-16','2013-11-17'), "%Y-%m-%d")
value <- c(10,15,20,21,3)
example<-as.DataFrame(data.frame(id=id,dates=dates, value))
I copy the example DF and add 1 day to the original date, then rename the column
example_p <- example
example_p$dates <- date_add(example_p$dates, 1)
colnames(example_p) <- c("id", "dates", "prev_value")
Finally, I merge the new DF to the original one
result <- select(merge(example, example_p, by = intersect(names(example),names(example_p))
, all.x = T), c("id_x", "dates_x", "value", "prev_value"))
showDF(result)
+----+----------+-----+----------+
|id_x| dates_x|value|prev_value|
+----+----------+-----+----------+
|22.0|2013-11-15| 20.0| null|
|11.0|2013-11-15| 10.0| null|
|11.0|2013-11-16| 15.0| 10.0|
|22.0|2013-11-16| 21.0| 20.0|
|22.0|2013-11-17| 3.0| 21.0|
+----+----------+-----+----------+
Obviously, this is somehow clumsy and I will be happy to give the points to anyone who can suggest a solution that would work faster than this.
I created a toy example of my code below.
In this toy example I would like to create a measure of all higher prices minus lower prices within a self-created reference group. So within each reference group, I would like to take each individual and subtract its price value from all higher price values from other individuals in the same group. I do not want to have negative differences. Then I would like to sum all these differences. In creating this code I found some help here:
http://www.stata.com/support/faqs/data-management/try-all-values-with-foreach/
However, the code didn't work perfectly for me, because my dataset is quite large (several 100K obs) and the examples on the website and my code only work until the numlist maximum of 1600 in Stata. (I am using version 12). The toy example with the auto dataset works, due to small size of the dataset.
I would like to ask if someone has an idea how to code this more efficiently, so that I can get around the numlist restriction. I thought about summing the differences directly without saving them in intermediate variables, but that also blow up the numlist restriction.
clear all
sysuse auto
ren headroom refgroup
bysort refgroup : egen pricerank = rank(price)
qui: su pricerank, meanonly
gen test = `r(max)'
su test
foreach i of num 1/`r(max)' {
qui: bys refgroup: gen intermediate`i' = price[_n+`i'] -price if price[_n+`i'] > price
}
egen price_diff = rowmax(intermediate*)
drop intermediate*
If I understand this correctly, this isn't even a problem that requires explicit loops. The sum of all higher prices is just the difference between two cumulative sums. You might need to think through what you want to do if prices are tied.
. clear
. set obs 10
obs was 0, now 10
. gen group = _n > 5
. set seed 2803
. gen price = ceil(1000 * runiform())
. bysort group (price) : gen sumhigherprices = sum(price)
. by group : replace sumhigherprices = sumhigherprices[_N] - sumhigherprices
(10 real changes made)
. list
+--------------------------+
| group price sumhig~s |
|--------------------------|
1. | 0 218 1448 |
2. | 0 264 1184 |
3. | 0 301 883 |
4. | 0 335 548 |
5. | 0 548 0 |
|--------------------------|
6. | 1 125 3027 |
7. | 1 213 2814 |
8. | 1 828 1986 |
9. | 1 988 998 |
10. | 1 998 0 |
+--------------------------+
Edit: For what the OP needs, there is an extra line
. by group : replace sumhigherprices = sumhigherprices - (_N - _n) * price
If I understand the wording of the problem correctly, maybe this can help. It uses joinby (new observations are created and depending on the size of the original database, you may or not hit the Stata hard-limit on number of observations). The code reproduces the results that would follow from the code of the original post. This is a second attempt. The code before this final edit did not provide the sought-after results. The wording of the problem was somewhat difficult for me to understand.
clear all
set more off
* Load data
sysuse auto
* Delete unnecessary vars
ren headroom refgroup
keep refgroup price
* Generate id´s based on rankings (sort)
bysort refgroup (price): gen id = _n
* Pretty list
order refgroup id
sort refgroup id price
list, sepby(refgroup)
* joinby procedure
tempfile main
save "`main'"
rename (price id) =0
joinby refgroup using "`main'"
list, sepby(refgroup)
* Do not compare with itself and drop duplicates
drop if id0 >= id
* Compute differences and max
gen dif = abs(price0 - price)
collapse (max) dif, by(refgroup id0)
list, sepby(refgroup)