\(\newcommand{\xb}{{\bf x}}
\newcommand{\betab}{\boldsymbol{\beta}}\)Before you use or distribute your estimation command, you should verify that it produces correct results and write a do-file that certifies that it does so. I discuss the processes of verifying and certifying an estimation command, and I present some techniques for writing a do-file that certifies mypoisson5, which I discussed in previous posts.

This is the twenty-fifth 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.

Verification versus certification

Verification is the process of establishing Read more…

This post was written jointly with Yulia Marchenko, Executive Director of Statistics, StataCorp.

As of update 03 Mar 2016, bayesmh provides a more convenient way of fitting distributions to the outcome variable. By design, bayesmh is a regression command, which models the mean of the outcome distribution as a function of predictors. There are cases when we do not have any predictors and want to model the outcome distribution directly. For example, we may want to fit a Poisson distribution or a binomial distribution to our outcome. This can now be done by specifying one of the four new distributions supported by bayesmh in the likelihood() option: dexponential(), dbernoulli(), dbinomial(), or dpoisson(). Previously, the suboption noglmtransform of bayesmh‘s option likelihood() was used to fit the exponential, binomial, and Poisson distributions to the outcome variable. This suboption continues to work but is now undocumented.

For examples, see Beta-binomial model, Bayesian analysis of change-point problem, and Item response theory under Remarks and examples in [BAYES] bayesmh.

We have also updated our earlier “Bayesian binary item response theory models using bayesmh” blog entry to use the new dbernoulli() specification when fitting 3PL, 4PL, and 5PL IRT models.

I make predict work after mypoisson5 by writing an ado-command that computes the predictions and by having mypoisson5 store the name of this new ado-command in e(predict). The ado-command that computes predictions using the parameter estimates computed by ado-command mytest should be named mytest_p, by convention. In the next section, I discuss mypoisson5_p, which computes predictions after mypoisson5. In section Storing the name of the prediction command in e(predict), I show that storing the name mypoisson5_p in e(predict) requires only a one-line change to mypoisson4.ado, which I discussed in Programming an estimation command in Stata: Adding analytical derivatives to a poisson command using Mata.

This is the twenty-fourth post in the Read more…

Overview

I describe how to generate random numbers and discuss some features added in Stata 14. In particular, Stata 14 includes a new default random-number generator (RNG) called the Mersenne Twister (Matsumoto and Nishimura 1998), a new function that generates random integers, the ability to generate random numbers from an interval, and several new functions that generate random variates from nonuniform distributions.

Random numbers from the uniform distribution

In the example below, we use runiform() to create 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…