Is it possible to change the confidence interval level (say from the default 95% to 90%) when using Stata's marginsplot command? It does not seem to accept the level(90) option or keep information from a preceding margins, level(90) command.
Please post exact code along with your explanation of what went wrong. It's difficult to assess what the problem is if you don't do that. This works fine (from the help file):
clear
set more off
webuse nhanes2
regress bpsystol agegrp##sex
margins agegrp
marginsplot, level(80)
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 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 a time series dataset with serious serial correlation problem, so I adopted Prais-Winsten estimator with iterated estimates to fix that. I did the regressions in Stata with the following command:
prais depvar indepvar indepvar2, vce(robust) rhotype(regress)
My colleague wanted to reproduce my results in SAS, so she used the following:
proc autoreg data=DATA;
model depvar = indepvar indepvar2/nlag=1 iter itprint method=YW;
run;
For the different specifications we ran, some of them roughly match, while others do not. Also I noticed that for each regression specification, Stata has many more iterations than SAS. I wonder if there is something wrong with my (or my colleague's) code.
Update
Inspired by Joe's comment, I modified my SAS code.
/*Iterated Estimation*/
proc autoreg data=DATA;
model depvar = indepvar indepvar2/nlag=1 itprint method=ITYW;
run;
/*Twostep Estimation*/
proc autoreg data=DATA;
model depvar = indepvar indepvar2/nlag=1 itprint method=YW;
run;
I have a few suggestions. Note that I'm not a real statistician and am not familiar with the specific estimators here, so this is just a quick read of the docs.
First off, the most likely issue is that it looks like SAS uses the OLS variance estimation method. That is, in your Stata code, you have vce(robust), which is in contrast to what I read SAS as using, the equivalent of vce(ols). See this page in the docs which explains how SAS does the Y-W method of autoregression, compared to this doc page that explains how Stata does it.
Second, you probably should not specify method=YW. SAS distinguishes between the simple Y-W estimation ("two-step" method) and iterated Y-W estimation. method=ITYW is what you want. You specify iter, so it may well be that you're getting this anyway as SAS tends to be smart about those sorts of things, but it's good to verify.
I would suggest actually turning the iterations off to begin with - have both do the two-step method (Stata option twostep, SAS by removing the iter request and specifying method=YW or no method specification). See how well they match there. Once you can get those to match, then move on to iterated; it's possible SAS has a different cutoff than Stata and may well not iterate past that.
I'd also suggest trying this with only one independent and dependent variable pair first, as it's possible the two programs handle things differently when you add in a second independent variable. Always start simple and then add complexity.
I am attempting to run a quantile regression on monthly observations (of mutual fund characteristics). What I would like to do is distribute my observations in quintiles for each month (my dataset comprises 99 months). I want to base the quintiles on a variable (lagged fund size i.e. Total Net Assets) that will be later employed as an independent variable to explain fund performance.
What I already tried to do is use the qreg command, but that uses quantiles based on the dependent variable not the independent variable that is needed.
Moreover I tried to use the xtile command to create the quintiles; however, the by: command is not supported.
. by Date: xtile QLagTNA= LagTNA, nq(5)
xtile may not be combined with by
r(190);
Is there a (combination of) command(s) which saves me from creating quintiles manually on a month-by-month basis?
Statistical comments first before getting to your question, which has two Stata answers at least.
Quantile regression is defined by prediction of quantiles of the response (what you call the dependent variable). You may or may not want to do that, but using quantile-based groups for predictors does not itself make a regression a quantile regression.
Quantiles (here quintiles) are values that divide a variable into bands of defined frequency. Here you want the 0, 20, 40, 60, 80, 100% points. The bands, intervals or groups themselves are not best called quantiles, although many statistically-minded people would know what you mean.
What you propose seems common in economics and business, but it is still degrading the information in the data.
All that said, you could always write a loop using forval, something like this
egen group = group(Date)
su group, meanonly
gen QLagTNA = .
quietly forval d = 1/`r(max)' {
xtile work = LagTNA if group == `d', nq(5)
replace QLagTNA = work if group == `d'
drop work
}
For more, see this link
But you will probably prefer to download a user-written egen function [correct term here] to do this
ssc inst egenmore
h egenmore
The function you want is xtile().
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