#STATA WINSORIZE HOW TO#
#STATA WINSORIZE MANUAL#

Some papers use the critical value of 3, which corresponds to 0.27% significance level, and seems to me not very reasonable. ThatвЂ™s why in literature we often see that data points with absolute values of studentized residuals greater than 2 will be deleted. Such a dummy variable would effectively absorb the observation and so remove its influence in determining the other coefficients in the model.вЂќ To be honest, I do not fully understand this explanation, but since rstuВ is a t statistics, the critical value for a traditional significance level should be applied, for example, 1.96 (or 2) for 5% significance level.

#STATA WINSORIZE MANUAL#

StataвЂ™s manual indicates that вЂњstudentized residuals can be interpreted as the t statistic for testing the significance of a dummy variable equal to 1 in the observation in question and 0 elsewhere. If the absolute value of rstuВ exceed certain critical values, the data point will be considered as an outlier and be deleted from the final sample.

Suppose the dependent variable is y, and independent variables are x1В and x2. The first step is to run a regression without specifying any vceВ parameter in Stata (i.e., not using robust or clustered error terms).

#STATA WINSORIZE HOW TO#

To install these two user-written commands, you can type:ДЄfter the installation, you can type help truncateJВ or help winsorizeJВ to learn how to use these two commands. I will save time to explain why, but simply highly recommend his work. In my opinion, the best Stata commands to do truncate and winsorize are truncateJВ and winsorizeJВ written by Judson Caskey. That said, this post is not going to answer that messy question instead, the purpose of this post is to summarize the Stata commands for commonly used methods of dealing with outliers (even if we are not sure whether these methods are appropriate-we all know that is true in accounting research!). In my opinion, only outliers resulting from apparent data errors should be deleted from the sample. I discuss in this post which Stata command to use to implement these four methods.ДЇirst of all, why and how we deal with potential outliers is perhaps one of the messiest issues that accounting researchers will encounter, because no one ever gives a definitive and satisfactory answer. The commonly used methods are: truncate, winsorize, studentized residuals, and CookвЂ™s distance. In accounting archival research, we often take it for granted that we must do something to deal with potential outliers before we run a regression.

Stata winsorize