Hi Arne, I believe I figured out why the Stata coefficient estimates differed from R's: in my case, the outcome response variable is binary, so the outcome equation is a probit model. From my reading of the sampleSelection paper, it seems that the Tobit-2 model has a continuous outcome response variable. The Stata command used was heckprob, which assumes both the outcome and the selection equations are probit models. When I compared the Stata heckman command with the R results, I found the estimates were the same.
Sorry for not picking up on that difference earlier. So it seems that selection() is perhaps not what I'm looking for, unless there is a way to specify a probit selection model. Is there a package out there that estimates probit models with Heckman sample selection? It looks like SemiParBIVProbit might work for me. - Clara On Friday, November 25, 2011 11:05:31 am Yuan Yuan wrote: > Hi Arne, > > Thanks for the reply. > > I am using R version 2.14.0 and sampleSelection version 0.6.12. > > I estimate the model by the 1-step ML method. However, when I use > the 2-step method, the standard errors are reported as NA. > > I use the selection() function, very basic call, something to the > effect of: selection(selectionFormula, outcomeFormula, data = > aDataFrame), where the formulas are very straightforward and basic > as well, y ~ x1 + x2 + ... + xp. > > I have read the associated paper, which is where I got the idea to > pass the coefficients from a seleciton object to the start argument. > > I will work on creating a minimal reproducible example; the dataset > is large and confidential, the models long-ish. > > - Clara > > On Friday, November 25, 2011 04:04:52 am Arne Henningsen wrote: > > On 25 November 2011 04:37, Yuan Yuan <y.y...@vt.edu> wrote: > > > Hello, > > > > > > I am working on reproducing someone's analysis which was done in > > > Stata. The analysis is estimation of a standard Heckman sample > > > selection model (Tobit-2), for which I am using the > > sampleSelection > > > > package and the selection() function. I have a few problems with > > the > > > > estimation: > > > > > > 1) The reported standard error for all estimates is Inf ... > > > vcov(selectionObject) yields Inf in every cell. > > > > > > 2) While the selection equation coefficient estimates are almost > > > exactly the same as the Stata results, the outcome equation > > > coefficient estimates are quite different (different sign in one > > case, > > > > order of magnitude difference in some other cases). > > > > > > 3) I can't seem to figure out how to specify the initial values > > for > > > > the MLE ... whatever argument I pass to start (even of the form > > > coef(selectionObject)), I get the following error: > > > Error in gr[, fixed] <- NA : (subscript) logical subscript too > > long > > > > I have to admit I am pretty confused by #1, I feel like I must > > be > > > > doing something wrong, missing something obvious, but I have no > > idea > > > > what. I figure #2 might be because the algorithms (selection and > > > Stata) are just finding different local maxima, but because of > > #3 I > > > > can't test that guess by using different initial values in > > selection. > > > > Let me know if I should provide any more information. Thanks in > > > advance for any pointers in the right direction. > > > > Yes, please provide more information (see also the posting guide > > [1]), > > > e.g. which version of R and which version of the sampleSelection > > package are you using? Do you estimate the model by the two-step > > approach or by the 1-step maximum likelihood method? Which > > commands > > > did use use? Can you send us a reproducible example? Have you read > > the > > > paper about using the sampleSelection package [2]? > > > > [1] http://www.r-project.org/posting-guide.html > > [2] http://www.jstatsoft.org/v27/i07 > > > > Best wishes from copenhagen, > > Arne ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
