I am getting error in Stata by using the following commands:
gen daily=date( Date,"MDY")
tsset daily
rolling cent=r(c_1), window(#) clear: centile lnreturn, centile(5)
but this is not giving me the result.
Your syntax looks legal apart from the use of #. Here is a different example that works:
webuse grunfeld, clear
tsset company year
rolling cent=r(c_1), window(7) clear: centile invest, centile(5)
Related
I am trying to tabulate frequencies for a variable divided in two groups. That is, I would like to see how much a variable takes value "Yes" divided by both region and sex. Now, this is easy to do in Stata using "tab" and option row, but I have trouble exporting it. To make it clear, I am able to build the table with absolute frequencies in this way:
eststo formalyes: estpost tab regionwb_c female if fin22a==1
eststo formalno: estpost tab regionwb_c female if fin22a==0
eststo formalt: estpost tab regionwb_c female
estout formalyes formalno formalt using summformal.tex, replace varlabels(`e(labels)') unstack booktabs ///
mgroups("Yes" "No" "Tot", pattern(1 1 1) prefix(\multicolumn{#span}{c}{) suffix(}) span erepeat(\cmidrule(lr){#span})) fragment
This, put in my latex code, produces this relatively nice table:table1
Now what I would like to do is to reproduce the exact same table, but to have the relative and not absolute frequencies there. Now normally to my understanding if you want the relative frequencies you can have
tab x y, row nofreq
but if you try to combine this with estpost it does not work. Are there any hints? I tried working it out with tabout, but all i was able to produce is this:
tabout regionwb_c female using trial.tex, replace percent style(tex) c(mean fin22a) sum
Which gives this:table2
Where, as you can see, I am pretty lost. I am sorry if the question sounds silly but I struggled finding results online or on the tabout manual. I hope somebody can help me.
I have not worked with tabout before, but maybe one way to work around it could be to just program new variables containing the male and female relative frequencies by regionwb_c using the egen command for example (like in this link enter link description here. Then you could just pass these relative frequencies variables in your table.
Could that maybe help you? Good Luck!
I have a rather simple question regarding the output of tabstat command in Stata.
To be more specific, I have a large panel dataset containing several hundred thousands of observations, over a 9 year period.
The context:
bysort year industry: egen total_expenses=total(expenses)
This line should create total expenses by year and industry (or sum of all expenses by all id's in one particular year for one particular industry).
Then I'm using:
tabstat total_expenses, by(country)
As far as I understand, tabstat should show in a table format the means of expenses. Please do note that ids are different from countries.
In this case tabstat calculates the means for all 9 years for all industries for a particular country, or it just the mean of one year and one industry by each country from my panel data?
What would happen if this command is used in the following context:
bysort year industry: egen mean_expenses=mean(expenses)
tabstat mean_expenses, by(country)
Does tabstat creates means of means? This is a little bit confusing.
I don't know what is confusing you about what tabstat does, but you need to be clear about what calculating means implies. Your dataset is far too big to post here, but for your sake as well as ours creating a tiny sandbox dataset would help you see what is going on. You should experiment with examples where the correct answer (what you want) is obvious or at least easy to calculate.
As a detail, your explanation that ids are different from countries is itself confusing. My guess is that your data are on firms and the identifier concerned identifies the firm. Then you have aggregations by industry and by country and separately by year.
bysort year industry: egen total_expenses = total(expenses)
This does calculate totals and assigns them to every observation. Thus if there are 123 observations for industry A and 2013, there will be 123 identical values of the total in the new variable.
tabstat total_expenses, by(country)
The important detail is that tabstat by default calculates and shows a mean. It just works on all the observations available, unless you specify otherwise. Stata has no memory or understanding of how total_expenses was just calculated. The mean will take no account of different numbers in each (industry, year) combination. There is no selection of individual values for (industry, year) combinations.
Your final question really has the same flavour. What your command asks for is a brute force calculation using all available data. In effect your calculations are weighted by the numbers of observations in whatever combinations of industry, country and year are being aggregated.
I suspect that you need to learn about two commands (1) collapse and (2) egen, specifically its tag() function. If you are using Stata 16, frames may be useful to you. That should apply to any future reader of this using a later version.
I am trying to compute a binomial confidence interval for a dummy variable after specifying the survey design in Stata with the svyset command but I get the following error: ci is not supported by svy with vce(linearized)
svyset [pweight=My_weight]
svy: ci Variable, binomial
I have also tried the following code:
ci Variable [pweight=My_weight], binomial
But got the error: pweight not allowed
Binomial confidence intervals are calculated as proportions in Stata 14 (Stata 13 uses binomial). This makes sense because the mean of a dummy variable is the proportion of 1's. Look at the help file here: http://www.stata.com/help.cgi?ci
So you likely want a command like:
ci proportions Variable [pweight=My_weight]
From the help file, it looks like only fweights may be allowed here.
Originally I thought that a better way might be to grab your CI from the means output. Here is an example modified from the svy help file.
webuse nhanes2f
svyset psuid [pweight=finalwgt]
svy: mean sex
But OP is right, this doesn't adjust for the binomial distribution.
I have trouble to generate a new variable which will be created for every month while having multiple entries for every month.
date1 x b
1925m12 .01213 .323
1925m12 .94323 .343
1926m01 .34343 .342
Code would look like this gen newvar = sum(x*b) but I want to create the variable for each month.
What I tried so far was
to create an index for the date1 variable with
sort date1
gen n=_n
and after that create a binary marker for when the date changes
with
gen byte new=date1!=date[[_n-1]
After that I received a value for every other month but I m not sure if this seems to be correct or not and thats why I would like someone have a look at this who could maybe confirm if that should be correct. The thing is as there are a lot of values its hard to control it manually if the numbers are correct. Hope its clear what I want to do.
Two comments on your code
There's a typo: date[[_n-1] should be date1[_n-1]
In your posted code there's no need for gen n = _n.
Maybe something along the lines of:
clear
set more off
*-----example data -----
input ///
str10 date1 x b
1925m12 .01213 .323
1925m12 .94323 .343
1926m01 .34343 .342
end
gen date2 = monthly(date1, "YM")
format %tm date2
*----- what you want -----
gen month = month(dofm(date2))
bysort month: gen newvar = sum(x*b)
list, sepby(month)
will help.
But, notice that the series of the cumulative sum can be different for each run due to the way in which Stata sorts and because month does not uniquely identify observations. That is, the last observation will always be the same, but the way in which you arrive at the sum, observation-by-observation, won't be. If you want the total, then use egen, total() instead of sum().
If you want to group by month/year, then you want: bysort date2: ...
The key here is the by: prefix. See, for example, Speaking Stata: How to move step by: step by Nick Cox, and of course, help by.
A major error is touched on in this thread which deserves its own answer.
As used with generate the function sum() returns cumulative or running sums.
As used with egen the function name sum() is an out-of-date but still legal and functioning name for the egen function total().
The word "function" is over-loaded here even within Stata. egen functions are those documented under egen and cannot be used in any other command or context. In contrast, Stata functions can be used in many places, although the most common uses are within calls to generate or display (and examples can be found even of uses within egen calls).
This use of the same name for different things is undoubtedly the source of confusion. In Stata 9, the egen function name sum() went undocumented in favour of total(), but difficulties are still possible through people guessing wrong or not studying the documentation really carefully.
I'm trying to store a series of scalars along the coefficients of a bootstrapped regression model. The code below looks like the example from the Stata [P]rogramming manual for postfile, which is apparently intended for use with such procedures.
The problem is with the // commented lines, which fail to work. More specifically, the problem seems to be that the syntax below worked in Stata 8 but fails to work in Stata 9+ after some change in the bootstrap procedure.
cap pr drop bsreg
pr de bsreg
reg mpg weight gear_ratio
predict yhat
qui sum yhat
// sca mu = r(mean)
// post sim (mu)
end
sysuse auto, clear
postfile sim mu using results , replace
bootstrap, cluster(foreign) reps(5) seed(6112): bsreg
postclose sim
use results, clear
Adding version 8 to the code did not solve the issue. Would anyone know what is wrong with this procedure, and how to fix it for execution in Stata 9+? The problem has been raised in the past and more recently, but without finding an answer.
Sorry for the long description, it's a long problem.
I've presented the issue as if it's a programming one because I'm using this code to replicate some health inequalities research. It's necessary to bootstrap the entire procedure, not just the reg model. I have some quibbles with the methodology, but nothing that would stop me from replicating the analysis.
Adding noisily to the bootstrap showed a problem with the predict command. Here's a fix using a tempvar macro.
cap pr drop bsreg
pr de bsreg
reg mpg weight gear_ratio
tempvar yhat
predict `yhat'
qui sum `yhat'
sca mu = r(mean)
post sim (mu)
end
sysuse auto, clear
postfile sim mu using results , replace
bootstrap, cluster(foreign) reps(5) seed(6112): bsreg
postclose sim
use results, clear