Author Archives: Kai Chen

We often see Heckman’s two steps in accounting literature. But how to do it in Stata? The two steps refer to the following two regressions: Outcome equation: y = X × b1 + u1 Selection equation: Dummy = Z × … Continue reading →

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 … Continue reading →

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 … Continue reading →

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 … Continue reading →

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 … Continue reading →

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 … Continue reading →

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 … Continue reading →