One of the most exciting times for us at StataCorp (and hopefully for you as well) is when we get to announce a new version of Stata, full of new features. Now, we hope to experience that feeling with you much more often.

Historically, we have released a new major version of Stata roughly every two years. We will still continue to do that, but most users will now have access to StataNow – a continuous-release Stata. StataNow gives you access to new features now, as soon as they are ready from the development, testing, and documentation groups. The features in StataNow are some of the same features that will also eventually appear in the next major release of Stata. StataNow users will get additional features on a continuous basis throughout the lifetime of a release.

You can read more about StataNow, including how to get it, and you can see its initial set of additional features. But let me tell you a little more about it here.

Many of you create features in Stata that you share with others via your own sites, the SSC archive, and the Stata Journal. And all of you write your own do-files as you perform your analyses in Stata. Knowing this, let me share with you a few technical details about StataNow.

First, StataNow is Stata. To be exact, the current Stata that most of you have is Stata 18.0. StataNow is Stata 18.5 (which we will call StataNow 18.5 from now on). When you are using StataNow, you should start your programs and do-files with version 18.5, just as you previously started them with version 18.0. Why is the version number different? Because StataNow is newer than Stata 18.0, and it is possible something in it will need to be version-controlled differently than in Stata 18. This is no different than when a new release comes out and it has a different version, 16.0, 17.0, 18.0, etc. As always, StataNow is backward compatible, so any programs, do-files, datasets, and so on from earlier versions will work, without changes, in StataNow.

What if we need to version-control something simultaneously in both Stata and StataNow? We would then release Stata 18.1 and StataNow 18.6.

The documentation and help files for Stata 18.0 and StataNow 18.5 are the same. StataNow features are included in them and clearly marked as such.

The dataset format in StataNow is the same as in Stata.

What are the new features in StataNow, and how often will we add features to StataNow? See the current set of new features. There is no set schedule for releasing new features, but we anticipate new features will be released fairly often – several times a year. We will release no new feature before its time, which means that anything released in StataNow is fully official, tested, validated, certified, and documented, just as all the features we put out in a new release of Stata.

When Stata 19 eventually comes out, it will of course include all the features that have come out along the way in StataNow as well as some additional new ones. Users of StataNow will automatically be able to upgrade to Stata 19 — actually, they will upgrade to StataNow 19.5 when Stata 19.0 comes out, and over time StataNow 19.5 will get additional features as soon as they are ready from the Stata elves.

We are excited to be able to give you the new features we add to Stata on a continuous basis, getting them into your hands sooner!

“The book that Stata programmers have been waiting for” is how the Stata Press describes my new book on Mata, the full title of which is

The Mata Book: A Book for Serious Programmers and Those Who Want to Be

The Stata Press took its cue from me in claiming that it this the book you have been waiting for, although I was less presumptuous in the introduction:

This book is for you if you have tried to learn Mata by reading the Mata Reference Manual and failed. You are not alone. Though the manual describes the parts of Mata, it never gets around to telling you what Mata is, what is special about Mata, what you might do with Mata, or even how Mata’s parts fit together. This book does that.

I’m excited about the book, but for a while I despaired of ever completing it. I started and stopped four times. I stopped because the drafts were boring. Read more…

\(
\newcommand{\xb}{{\bf x}}
\newcommand{\gb}{{\bf g}}
\newcommand{\Hb}{{\bf H}}
\newcommand{\Gb}{{\bf G}}
\newcommand{\Eb}{{\bf E}}
\newcommand{\betab}{\boldsymbol{\beta}}
\)I write ado-commands that estimate the parameters of an exponential conditional mean (ECM) model and a probit conditional mean (PCM) model by nonlinear least squares, using the methods that I discussed in the post Programming an estimation command in Stata: Nonlinear least-squares estimators. These commands will either share lots of code or repeat lots of code, because they are so similar. It is almost always better to share code than to repeat code. Shared code only needs to be changed in one place to add a feature or to fix a problem; repeated code must be changed everywhere. I introduce Mata libraries to share Mata functions across ado-commands, and I introduce wrapper commands to share ado-code.

This is the 27th post in the series Programming an estimation command in Stata. I recommend that you start at the beginning. See Programming an estimation command in Stata: A map to posted entries for a map to all the posts in this series.

Ado-commands for ECM and PCM models

I now convert the examples of Read more…

\(\newcommand{\xb}{{\bf x}}
\newcommand{\gb}{{\bf g}}
\newcommand{\Hb}{{\bf H}}
\newcommand{\Gb}{{\bf G}}
\newcommand{\Eb}{{\bf E}}
\newcommand{\betab}{\boldsymbol{\beta}}\)I want to write ado-commands to estimate the parameters of an exponential conditional mean (ECM) model and probit conditional mean (PCM) model by nonlinear least squares (NLS). Before I can write these commands, I need to show how to trick optimize() into performing the Gauss–Newton algorithm and apply this trick to these two problems.

This is the 26th post in the series Programming an estimation command in Stata. I recommend that you start at the beginning. See Programming an estimation command in Stata: A map to posted entries for a map to all the posts in this series.

Gauss–Newton algorithm

Gauss–Newton algorithms frequently perform better than Read more…

\(\newcommand{\xb}{{\bf x}}
\newcommand{\betab}{\boldsymbol{\beta}}\)Using analytically computed derivatives can greatly reduce the time required to solve a nonlinear estimation problem. I show how to use analytically computed derivatives with optimize(), and I discuss mypoisson4.ado, which uses these analytically computed derivatives. Only a few lines of mypoisson4.ado differ from the code for mypoisson3.ado, which I discussed in Programming an estimation command in Stata: Allowing for robust or cluster–robust standard errors in a poisson command using Mata.

This is the twenty-third post in the series Programming an estimation command in Stata. I recommend that you start at the beginning. See Programming an estimation command in Stata: A map to posted entries for a map to all the posts in this series.

Analytically computed derivatives for Poisson

The contribution of the i(th) observation to the log-likelihood function for the Poisson maximum-likelihood estimator is Read more…

mypoisson3.ado adds options for a robust or a cluster–robust estimator of the variance–covariance of the estimator (VCE) to mypoisson2.ado, which I discussed in Programming an estimation command in Stata: Handling factor variables in a poisson command using Mata. mypoisson3.ado parses the vce() option using the techniques I discussed in Programming an estimation command in Stata: Adding robust and cluster–robust VCEs to our Mata based OLS command. Below, I show how to use optimize() to compute the robust or cluster–robust VCE.

I only discuss what is new in the code for mypoisson3.ado, assuming that you are familiar with mypoisson2.ado.

This is the twenty-second post in the series Programming an estimation command in Stata. I recommend that you start at the beginning. See Programming an estimation command in Stata: A map to posted entries for a map to all the posts in this series.

A poisson command with options for a robust or a cluster–robust VCE

mypoisson3 computes Poisson-regression results in Mata. The syntax of the mypoisson3 command is

mypoisson3 depvar indepvars [if] [in] [, vce(robust | cluster clustervar) noconstant]

where indepvars can contain factor variables or time-series variables.

In the remainder of this post, I discuss Read more…

mypoisson2.ado handles factor variables and computes its Poisson regression results in Mata. I discuss the code for mypoisson2.ado, which I obtained by adding the method for handling factor variables discussed in Programming an estimation command in Stata: Handling factor variables in optimize() to mypoisson1.ado, discussed in Programming an estimation command in Stata: A poisson command using Mata.

This is the twenty-first post in the series Programming an estimation command in Stata. I recommend that you start at the beginning. See Programming an estimation command in Stata: A map to posted entries for a map to all the posts in this series.

A Poisson command with Mata computations

mypoisson2 computes Poisson regression results in Mata. The syntax of the mypoisson2 command is

mypoisson2 depvar indepvars [if] [in] [, noconstant]

where indepvars can contain factor variables or time-series variables.

In the remainder of this post, I discuss Read more…

\(
\newcommand{\xb}{{\bf x}}
\newcommand{\betab}{\boldsymbol{\beta}}\)I discuss a method for handling factor variables when performing nonlinear optimization using optimize(). After illustrating the issue caused by factor variables, I present a method and apply it to an example using optimize().

This is the twenty post in the series Programming an estimation command in Stata. I recommend that you start at the beginning. See Programming an estimation command in Stata: A map to posted entries for a map to all the posts in this series.

How poisson handles factor variables

Consider the Poisson regression in which I include a full set of indicator variables created from Read more…

\(
\newcommand{\xb}{{\bf x}}
\newcommand{\betab}{\boldsymbol{\beta}}\)I discuss mypoisson1, which computes Poisson-regression results in Mata. The code in mypoisson1.ado is remarkably similar to the code in myregress11.ado, which computes ordinary least-squares (OLS) results in Mata, as I discussed in Programming an estimation command in Stata: An OLS command using Mata.

I build on previous posts. I use the structure of Stata programs that use Mata work functions that I discussed previously in Programming an estimation command in Stata: A first ado-command using Mata and Programming an estimation command in Stata: An OLS command using Mata. You should be familiar with Read more…

\(
\newcommand{\xb}{{\bf x}}
\newcommand{\betab}{\boldsymbol{\beta}}\)I show how to use optimize() in Mata to maximize a Poisson log-likelihood function and to obtain estimators of the variance–covariance of the estimator (VCE) based on independent and identically distributed (IID) observations or on robust methods.

This is the eighteenth post in the series Programming an estimation command in Stata. I recommend that you start at the beginning. See Programming an estimation command in Stata: A map to posted entries for a map to all the posts in this series.

Using optimize()

There are many optional choices that one may make when solving a nonlinear optimization problem, but there are very few that one must make. The optimize*() functions in Mata handle this problem by making a set of default choices for you, requiring that you specify a few things, and allowing you to change any of the default choices.

When I use optimize() to solve a Read more…