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

\(\newcommand{\betab}{\boldsymbol{\beta}}
\newcommand{\xb}{{\bf x}}
\newcommand{\yb}{{\bf y}}
\newcommand{\gb}{{\bf g}}
\newcommand{\Hb}{{\bf H}}
\newcommand{\thetab}{\boldsymbol{\theta}}
\newcommand{\Xb}{{\bf X}}
\)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…

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…

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…

I discuss a command that computes ordinary least-squares (OLS) results in Mata, paying special attention to the structure of Stata programs that use Mata work functions.

This command builds on several previous posts; at a minimum, you should be familiar with Programming an estimation command in Stata: A first ado-command using Mata and Programming an estimation command in Stata: Computing OLS objects in Mata.

This is the fifteenth 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.

An OLS command with Mata computations

The Stata command myregress11 computes the results in Mata. The syntax of the myregress11 command is

myregress11 depvar [indepvars] [if] [in] [, noconstant]

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

In the remainder of this post, I discuss the code for myregress11.ado. I recommend that you click on the file name to download the code. To avoid scrolling, view the code in the do-file editor, or your favorite text editor, to see the line numbers.

I do not discuss Read more…

\(\newcommand{\epsilonb}{\boldsymbol{\epsilon}}
\newcommand{\ebi}{\boldsymbol{\epsilon}_i}
\newcommand{\Sigmab}{\boldsymbol{\Sigma}}
\newcommand{\betab}{\boldsymbol{\beta}}
\newcommand{\eb}{{\bf e}}
\newcommand{\xb}{{\bf x}}
\newcommand{\xbit}{{\bf x}_{it}}
\newcommand{\xbi}{{\bf x}_{i}}
\newcommand{\zb}{{\bf z}}
\newcommand{\zbi}{{\bf z}_i}
\newcommand{\wb}{{\bf w}}
\newcommand{\yb}{{\bf y}}
\newcommand{\ub}{{\bf u}}
\newcommand{\Xb}{{\bf X}}
\newcommand{\Mb}{{\bf M}}
\newcommand{\Xtb}{\tilde{\bf X}}
\newcommand{\Wb}{{\bf W}}
\newcommand{\Vb}{{\bf V}}\)I present the formulas for computing the ordinary least-squares (OLS) estimator and show how to compute them in Mata. This post is a Mata version of Programming an estimation command in Stata: Using Stata matrix commands and functions to compute OLS objects. I discuss the formulas and the computation of independence-based standard errors, robust standard errors, and cluster-robust standard errors.

This is the fourteenth 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.

OLS formulas

Recall that the OLS point estimates are given by

\[
\widehat{\betab} =
\left( \sum_{i=1}^N \xb_i’\xb_i \right)^{-1}
\left(
\sum_{i=1}^N \xb_i’y_i
\right)
\]

where \(\xb_i\) is the \(1\times k\) vector of independent variables, \(y_i\) is the dependent variable for each of the \(N\) sample observations, and the model for \(y_i\) is

\[
y_i = \xb_i\betab’ + \epsilon_i
\]

If the \(\epsilon_i\) are independently and identically distributed (IID), we estimate Read more…

I discuss a sequence of ado-commands that use Mata to estimate the mean of a variable. The commands illustrate a general structure for Stata/Mata programs. This post builds on Programming an estimation command in Stata: Mata 101, Programming an estimation command in Stata: Mata functions, and Programming an estimation command in Stata: A first ado-command.

This is the thirteenth 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 Mata in ado-programs

I begin by reviewing the structure in mymean5.ado, which I discussed Read more…

I show how to write a function in Mata, the matrix programming language that is part of Stata. This post uses concepts introduced in Programming an estimation command in Stata: Mata 101.

This is the twelfth 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.

Mata functions

Commands do work in Stata. Functions do work in Mata. Commands operate on Stata objects, like variables, and users specify options to alter the behavior. Mata functions accept arguments, operate on the arguments, and may return a result or alter the value of an argument to contain a result.

Consider myadd() defined below.

mata:
function myadd(X, Y)
{
    A = X + Y
    return(A)
}
end

myadd() accepts two arguments, X and Y, puts the sum of X and Y into A, and returns A. For example, Read more…

I introduce Mata, the matrix programming language that is part of Stata.

This is the eleventh 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. Read more…

I make two improvements to the command that implements the ordinary least-squares (OLS) estimator that I discussed in Programming an estimation command in Stata: Allowing for options. First, I add an option for a cluster-robust estimator of the variance-covariance of the estimator (VCE). Second, I make the command accept the modern syntax for either a robust or a cluster-robust estimator of the VCE. In the process, I use subroutines in my ado-program to facilitate the parsing, and I discuss some advanced parsing tricks.

This is the tenth 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. Read more…