Kai Chen

Please read this post for how to display the results in a ready-for-use format.

UCLA IDRE has posted an article (link) that may provide a bit more explanation. UCLA IDRE is a great resource for learning statistical analysis. A big thank you to them.

Oftentimes we would like to display Pearson correlations below the diagonal and Spearman correlations above the diagonal. Two built-in commands, pwcorr and spearman, can do the job. However, we have to manually combine Stata output tables when producing the correlation table in the manuscript, which is time-consuming.

I find this fantastic module written by Daniel Klein. His command will return one table that combines Pearson and Spearman correlations and needs the fewest further edits. Thanks Daniel and please find his work here.

A sample command is as follows:

corsp varlist, pw sig

To install Daniel’s module, type ssc install corsp in Stata’s command window.

A good technical comparison of Pearson and Spearman correlations can be found here.

The default type of GVEKY in Compustat is string. Sometimes, we need it to be a numerical type in Stata (e.g., when we want to use the super handy command tsset). The command to convert string GVKEY to numerical GVEKY is very simple:

destring gvkey, replace

The command to revert numerical GVKEY to string GVKEY with leading zeros is as follows:

tostring gvkey, replace format(%06.0f)

If we want to evaluate the predictive ability of a logit or probit model, Kim and Skinner (2012, JAE, Measuring securities litigation risk) suggest that

A better way of comparing the predictive ability of different models is to use the Receiver Operating Characteristic, or ROC curve (e.g., Hosmer and Lemeshow, 2000, Chapter 5). This curve ‘‘plots the probability of detecting a true signal (sensitivity) and false signal (1—specificity) for the entire range of possible cutpoints’’ (p. 160, our emphasis). The area under the ROC curve (denoted AUC) provides a measure of the model’s ability to discriminate. A value of 0.5 indicates no ability to discriminate (might as well toss a coin) while a value of 1 indicates perfect ability to discriminate, so the effective range of AUC is from 0.5 to 1.0. Hosmer-Lemeshow (2000, p. 162) indicate that AUC of 0.5 indicates no discrimination, AUC of between 0.7 and 0.8 indicates acceptable discrimination, AUC of between 0.8 and 0.9 indicates excellent discrimination, and AUC greater than 0.9 is considered outstanding discrimination.

The Stata command to report AUC is as follows:

logit y x1 x2 or probit y x1 x2

lroc, nograph

The most recent edition of the book Kim and Skinner refer to is Hosmer, D. W., Jr., S. A. Lemeshow, and R. X. Sturdivant. 2013. Applied Logistic Regression. 3rd ed. Hoboken, NJ: Wiley.

A technical note from Stata: lroc requires that the current estimation results be from logistic, logit, probit, or ivprobit.

A side question: what’s the difference between logistic and logit regression? Nick Cox’s short answer is: “same thing with different emphases in reporting.” (something like one gives you the odds ratios, the other gives you the log of the odds ratios.)—thanks to a post on Stack Overflow.

Suppose we are going to calculate the skewness of 12 monthly returns. The 12 returns may be stored in a row (Figure 1) or in a column (Figure 2). This post discusses how to calculate the skewness in these two situations. Please note there are several formulae for skewness out there, which may yield different results. This post uses the formula that yields the same skewness as the Stata command sum var, detail reports.

Figure 1: Returns are stored in a row

Figure 2: Returns are stored in a column

If returns are stored in a row

Stata does not provide a command to calculate the skewness in this situation. The following Stata commands will do the job.

If returns are stored in a column

Stata provides a command to calculate skewness in this situation (egen and skewness). However, the computation is extremely slow if we have millions of observations. I would suggest calculating the skewness manually as follows:

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. The commonly used methods are: truncate, winsorize, studentized residuals, and Cook’s distance. I discuss in this post which Stata command to use to implement these four methods.

First 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. In my opinion, only outliers resulting from apparent data errors should be deleted from the sample. 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!). Let’s start.

Truncate and winsorize

In my opinion, the best Stata commands to do truncate and winsorize are truncateJ and winsorizeJ written by Judson Caskey. I will save time to explain why, but simply highly recommend his work. Please see his website here.

To install these two user-written commands, you can type:

net from https://sites.google.com/site/judsoncaskey/data
net install utilities.pkg

After the installation, you can type help truncateJ or help winsorizeJ to learn how to use these two commands.

Studentized residuals

The first step is to run a regression without specifying any vce parameter in Stata (i.e., not using robust or clustered error terms). Suppose the dependent variable is y, and independent variables are x1 and x2. The first step should look like this:

regress y x1 x2

Then, use the predict command:

predict rstu if e(sample), rstudent

If the absolute value of rstu exceed certain critical values, the data point will be considered as an outlier and be deleted from the final sample. Stata’s manual indicates that “studentized 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. Such a dummy variable would effectively absorb the observation and so remove its influence in determining the other coefficients in the model.” To be honest, I do not fully understand this explanation, but since rstu 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. That’s why in literature we often see that data points with absolute values of studentized residuals greater than 2 will be deleted. Some papers use the critical value of 3, which corresponds to 0.27% significance level, and seems to me not very reasonable.

Now use the following command to drop “outliers” based on the critical value of 2:

drop if abs(rstu) > 2

The last step is to re-run the regression, but this time we can add appropriate vce parameters to address additional issues such as heteroskedasticity:

regress y x1 x2, vce(robust), or

regress y x1 x2, vce(cl gvkey)

Cook’s distance

This method is similar to studentized residuals. We predict a specific residual, namely Cook’s distance, and then delete any data points with Cook’s distance greater than 4/N (Cook’s distance is always positive).

regress y x1 x2

predict cooksd if e(sample), cooksd

drop if cooksd > critical value

Next, re-run the regression with appropriate vce parameters:

regress y x1 x2, vce(robust), or

regress y x1 x2, vce(cl gvkey)

Lastly, I thank the authors of the following articles which I benefit from:

https://www3.nd.edu/~rwilliam/stats2/l24.pdf

https://www.stat-d.si/mz/mz16/coend16.pdf

A more formal and complete econometrics book is Belsley, D. A., E. Kuh, and R. E. Welsch. 1980. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New York: Wiley.