Category Archives: Stata

I ran a test on Stata running speed on my newest MacBook Pro (14-inch, 2021) and two old Macs—iMac (27-inch, 2019) and MacBook Pro (16-inch, 2019). Technical specifications: MacBook Pro (14-inch, 2021): CPU Apple M1 Pro (10-core), memory 16G, SSD … Continue reading →

Most propensity score matching (PSM) examples are using cross-sectional data instead of panel data. However, accounting research often uses panel data (i.e., observations with two subscripts i and t, e.g. firm-years) in a difference-in-differences (DID) research design, so that there are … Continue reading →

A Chow test is simply a test of whether the coefficients estimated over one group of the data are equal to the coefficients estimated over another. I find two useful articles from Stata’s official website: Can you explain Chow tests? … Continue reading →

Stata is a case-sensitive application. Sometimes this will cause a trouble. So, we may want to change variable names or values of variables to all lowercase before we start processing data. This post gives a fast way to do this. … Continue reading →

egen compdatadate=eom(fiscalmonth fiscalyear) format compdatadate %td To be continued …

tabulate varname command is handy in Stata, but sometimes it returns a too long result, if varname contains too many unique values. The third-party command, groups, will solve the problem by showing top values only. Please use ssc install groups … Continue reading →

Stata is a little bit awkward when using and and or in if statement, compared to SAS. For example: In SAS, we can write if 2001 <= fyear <= 2010. But in Stata, we usually write: if fyear >= 2001 … Continue reading →

Sometimes we may want to produce the following table to compare the mean and median of two groups: First of all, please refer to this post to see Stata commands to test equality of mean and median. However, it is … Continue reading →

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 →