Have you every wanted to make an “easy” calculation–say, after fitting a model–and gotten lost because you just weren’t sure where to find the degrees of freedom of the residual or the standard error of the coefficient? Have you ever been in the midst of constructing an “easy” calculation and was suddenly unsure just what e(df_r) really was? I have a solution.

It’s called Stata’s expression builder. You can get to it from the display dialog (Data->Other Utilities->Hand Calculator) Read more…

Leap seconds are the extra seconds inserted every so often to keep precise atomic clocks better synchronized with the rotation of the Earth. Scheduled for June 30th is the extra second 23:59:60 inserted between 23:59:59 and 00:00:00. Or maybe not.

Tomorrow or Friday a vote may be held at the International Telecommuncation Union (ITU) meeting in Geneva to abolish the leap second from the definition of UTC (Coordinated Universial Time). Which would mean StataCorp would not have to post an update to Stata to keep the %tC format working correctly. Read more…

If you have a bug in your evaluator program, nl will produce, most probably, the following error:

your program returned 198
verify that your program is a function evaluator program
r(198);

The error indicates that your program cannot be evaluated.

The best way to spot any issues in your evaluator program is to run it interactively. You just need to define your sample (usually observations where none of the variables are missing), and a matrix with values for your parameters. Let me show you an example with nlces2. This is the code to fit the CES production function, from the documentation for the nl command: Read more…

Dembe, Partridge, and Geist (2011, pdf), in a paper recently published in BMC Health Services Research, report that Stata and SAS were “overwhelmingly the most commonly used software applications employed (in 46% and 42.6% of articles respectively)”. The articles referred to were those in health services research studies published in the U.S.

Good company. Both are, in our humble opinion, excellent packages, although we admit to have a preference for one of them.

We should mention that the authors report that SAS usage grew considerably during the study period, and that Stata usage held roughly constant, a conclusion that matches the results in their Table 1, an extract of which is Read more…

xtmixed was built from the ground up for dealing with multilevel random effects — that is its raison d’être. sem was built for multivariate outcomes, for handling latent variables, and for estimating structural equations (also called simultaneous systems or models with endogeneity). Can sem also handle multilevel random effects (REs)? Do we care?

This would be a short entry if either answer were “no”, so let’s get after the first question. Read more…

I’m still recycling my talk called “Mata, The Missing Manual” at user meetings, a talk designed to make Mata more approachable. One of the things I say late in the talk is, “Unless you already know what pointers are and know you need them, ignore them. You don’t need them.” And here I am writing about, of all things, pointers. Well, I exaggerated a little in my talk, but just a little.

Before you take my previous advice and stop reading, let me explain: Mata serves a number of purposes and one of them is as the primary langugage we at StataCorp use to implement new features in Stata. I’m not referring to mock ups, toys, and experiments, I’m talking about ready-to-ship code. Stata 12’s Structural Equation Modeling features are written in Mata, so is Multiple Imputation, so is Stata’s optimizer that is used by nearly all estimation commands, and so are most features. Mata has a side to it that is exceedingly serious and intended for use by serious developers, and every one of those features are available to users just as they are to StataCorp developers. This is one of the reasons there are so many user-written commands are available for Stata. Even if you don’t use the serious features, you benefit. Read more…

Do you ever fit regressions of the form

ln(y_j) = b₀ + b₁x_1j + b₂x_2j + … + b_kx_kj + ε_j

by typing

. generate lny = ln(y)

. regress lny x1 x2 … xk

The above is just an ordinary linear regression except that ln(y) appears on the left-hand side in place of y. Read more…

The 2011 Stata Conference in Chicago ended last Friday, and a good time was had by all.

The two days had the usual wide array of talks, given by researchers in Econometrics, Sociology, Medicine, and Statistics, together with three of us from StataCorp—Bill Gould, David Drukker, and me.

The conference was held in the Gleacher center on the banks of the Chicago River in Chicago (of course), which is a fine facility. I know it sounds mundane, but the acoustics in the lecture hall were excellent, making it very easy for speakers and questions to be heard clearly. Read more…

We are pleased to announce a new version of Stata: Stata 12. You can order it today, it starts shipping on July 25, and you can find out about it at www.stata.com/stata12/.

Here are the highlights of what’s new: Read more…

In part I, I wrote about precision issues in English. If you enjoyed that, you may want to stop reading now, because I’m about to go into the technical details. Actually, these details are pretty interesting.

For instance, I offered the following formula for calculating error due to float precision: Read more…