Programming an estimation command in Stata: Handling factor variables in a poisson command using Mata

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…

Testing model specification and using the program version of gmm

This post was written jointly with Joerg Luedicke, Senior Social Scientist and Statistician, StataCorp.

The command gmm is used to estimate the parameters of a model using the generalized method of moments (GMM). GMM can be used to estimate the parameters of models that have more identification conditions than parameters, overidentified models. The specification of these models can be evaluated using Hansen’s J statistic (Hansen, 1982).

We use gmm to estimate the parameters of a Poisson model with an endogenous regressor. More instruments than regressors are available, so the model is overidentified. We then use estat overid to calculate Hansen’s J statistic and test the validity of the overidentification restrictions.

In previous posts Read more…

Programming an estimation command in Stata: Handling factor variables in optimize()

\(
\newcommand{\xb}{{\bf x}}
\newcommand{\betab}{\boldsymbol{\beta}}\)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…

Handling gaps in time series using business calendars

Time-series data, such as financial data, often have known gaps because there are no observations on days such as weekends or holidays. Using regular Stata datetime formats with time-series data that have gaps can result in misleading analysis. Rather than treating these gaps as missing values, we should adjust our calculations appropriately. I illustrate a convenient way to work with irregularly spaced dates by using Stata’s business calendars.

In nasdaq.dta, I have daily data on Read more…

Programming an estimation command in Stata: A poisson command using Mata

\(
\newcommand{\xb}{{\bf x}}
\newcommand{\betab}{\boldsymbol{\beta}}\)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…

Programming an estimation command in Stata: Using optimize() to estimate Poisson parameters

\(
\newcommand{\xb}{{\bf x}}
\newcommand{\betab}{\boldsymbol{\beta}}\)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…

Programming an estimation command in Stata: A review of nonlinear optimization using Mata

\(\newcommand{\betab}{\boldsymbol{\beta}}
\newcommand{\xb}{{\bf x}}
\newcommand{\yb}{{\bf y}}
\newcommand{\gb}{{\bf g}}
\newcommand{\Hb}{{\bf H}}
\newcommand{\thetab}{\boldsymbol{\theta}}
\newcommand{\Xb}{{\bf X}}
\)I review the theory behind nonlinear optimization and get more practice in Mata programming by implementing an optimizer in Mata. In real problems, I recommend using the optimize() function or moptimize() function instead of the one I describe here. In subsequent posts, I will discuss optimize() and moptimize(). This post will help you develop your Mata programming skills and will improve your understanding of how optimize() and moptimize() work.

This is the seventeenth 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 quick review of nonlinear optimization

We want to maximize a real-valued function \(Q(\thetab)\), where \(\thetab\) is a \(p\times 1\) vector of parameters. Minimization is done by maximizing \(-Q(\thetab)\). We require that \(Q(\thetab)\) is twice, continuously differentiable, so that we can use a second-order Taylor series to approximate \(Q(\thetab)\) in a neighborhood of the point \(\thetab_s\),

\[
Q(\thetab) \approx Q(\thetab_s) + \gb_s'(\thetab -\thetab_s)
+ \frac{1}{2} (\thetab -\thetab_s)’\Hb_s (\thetab -\thetab_s)
\tag{1}
\]

where \(\gb_s\) is the \(p\times 1\) vector of first derivatives of \(Q(\thetab)\) evaluated at \(\thetab_s\) and \(\Hb_s\) is the \(p\times p\) matrix of second derivatives of \(Q(\thetab)\) evaluated at \(\thetab_s\), known as the Hessian matrix.

Nonlinear maximization algorithms start with Read more…

Programming an estimation command in Stata: Adding robust and cluster-robust VCEs to our Mata-based OLS command


I show how to use the undocumented command _vce_parse to parse the options for robust or cluster-robust estimators of the variance-covariance of the estimator (VCE). I then discuss myregress12.ado, which performs its computations in Mata and computes VCE estimators based on independently and identically distributed (IID) observations, robust methods, or cluster-robust methods.

myregress12.ado performs ordinary least-squares (OLS) regression, and it extends myregress11.ado, which I discussed in Programming an estimation command in Stata: An OLS command using Mata. To get the most out of this post, you should be familiar with Programming an estimation command in Stata: Using a subroutine to parse a complex option and Programming an estimation command in Stata: Computing OLS objects in Mata.

This is the sixteenth 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.

Parsing the vce() option

I used ado-subroutines to simplify the parsing of the options vce(robust) and vce(cluster cvarname) in myregress10.ado; see Programming an estimation command in Stata: Using a subroutine to parse a complex option. Part of the point was to Read more…

Bayesian binary item response theory models using bayesmh

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

Table of Contents

Overview
1PL model
2PL model
3PL model
4PL model
5PL model
Conclusion

Overview

Item response theory (IRT) is used for modeling the relationship between the latent abilities of a group of subjects and the examination items used for measuring their abilities. Stata 14 introduced a suite of commands for fitting IRT models using maximum likelihood; see, for example, the blog post Spotlight on irt by Rafal Raciborski and the [IRT] Item Response Theory manual for more details. In this post, we demonstrate how to fit Bayesian binary IRT models by using the redefine() option introduced for the bayesmh command in Stata 14.1. We also use the likelihood option dbernoulli() available as of the update on 03 Mar 2016 for fitting Bernoulli distribution. If you are not familiar with the concepts and jargon of Bayesian statistics, you may want to watch the introductory videos on the Stata Youtube channel before proceeding.

Introduction to Bayesian analysis, part 1 : The basic concepts
Introduction to Bayesian analysis, part 2: MCMC and the Metropolis-Hastings algorithm

We use the abridged version of the mathematics and science data from DeBoeck and Wilson (2004), masc1. The dataset includes 800 student responses to 9 test questions intended to measure mathematical ability.

The irt suite fits IRT models using data in the wide form – one observation per subject with items recorded in separate variables. To fit IRT models using bayesmh, we need data in the long form, where items are recorded as multiple observations per subject. We thus reshape the dataset in a long form: we have a single binary response variable, y, and two index variables, item and id, which identify the items and subjects, respectively. This allows us to Read more…

Programming an estimation command in Stata: A map to posted entries

I have posted a series of entries about programming an estimation command in Stata. They are best read in order. The comprehensive list below allows you to read them from first to last, at your own pace.

  1. Programming estimators in Stata: Why you should

    To help you write Stata commands that people want to use, I illustrate how Stata syntax is predictable and give an overview of the estimation-postestimation structure that you will want to emulate in your programs.

  2. Programming an estimation command in Stata: Where to store your stuff

    I discuss the difference between scripts and commands, and I introduce some essential programming concepts and constructions that I use to write the scripts and commands.

  3. Programming an estimation command in Stata: Global macros versus local macros

    I discuss a pair of examples that illustrate the differences between global macros and local macros.

  4. Programming an estimation command in Stata: A first ado-command

    I discuss the code for a simple estimation command to focus on the details of how to implement an estimation command. The command that I discuss estimates the mean by the sample average. I begin by reviewing the formulas and a do-file that implements them. I subsequently introduce Read more…