Tobit Regression with unbalanced Panel Data
Hi Although I cannot help you with your actual problem, you shall start with checking your data before doing any analysis. We do not have your data so it is hard to say what can be wrong. At least you shall provide result of str(T1) and/or str(mydata) The first message is not an error but a warning that tells you about coercing some log values to NaN which can result e.g. from negative values. log(-1) [1] NaN Warning message: In log(-1) : NaNs produced and probably some further calculation in summary function does not like it and throws error. But without data it is only a guess. And BTW, you shall post plain text not HTML. Cheers Petr
-----Original Message-----
From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Vanessa
Romero
Sent: Wednesday, January 4, 2017 10:28 AM
To: r-help at r-project.org
Subject: [R] Tobit Regression with unbalanced Panel Data
Hello,
I am doing Tobit Regression in R, because my dependent variable is censored
at 0. I have unbalanced panel data, for 6 years, 107 companies. I use package
CensReg.
I have imported my database(T1).
I use pdata.frame to specify the structure of my panel data. Like:
*mydata<- pdata.frame (T1, index = c("firm", "year")) *
Afterwards:
*Tob <- censReg(formula=Imp ~ Bath + CEOTurnover + ChangeOCF + E +
Sales + ROE + GTA + Size , data = mydata, method="BHHH") * (as explained
here:
https://cran.r-project.org/web/packages/censReg/vignettes/censReg.pdf)
I got here error message:
*Warnmeldung: In log(rEff$ercomp$sigma$id) : NaNs wurden erzeugt*
Another error message when *summary(Tob)*
*Call: censReg(formula = Imp ~ Bath + CEOTurnover + ChangeOCF + E + Sales
+ ROE + GTA + Size, data = mydata, method = "BHHH") Observations: Total
Left-censored Uncensored Right-censored 606 469 137 0 Coefficients: Fehler
in printCoefmat(coef(x, logSigma = logSigma), digits = digits) : 'x' must be
coefficient matrix/data frame*
I am new to statistics and to R, what could be the problem or would you
suggest using other package.
Thank you,
Vanessa
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