I use the syntax command to improve the command that implements the ordinary least-squares (OLS) estimator that I discussed in Programming an estimation command in Stata: A first command for OLS. I show how to require that all variables be numeric variables and how to make the command accept time-series operated variables.

This is the seventh 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…

\(
\newcommand{\betab}{\boldsymbol{\beta}}
\newcommand{\xb}{{\bf x}}
\newcommand{\yb}{{\bf y}}
\newcommand{\Xb}{{\bf X}}
\)I show how to write a Stata estimation command that implements the ordinary least-squares (OLS) estimator by explaining the code. I use concepts that I introduced in previous #StataProgramming posts. In particular, I build on Programming an estimation command in Stata: Using Stata matrix commands and functions to compute OLS objects, in which I recalled the OLS formulas and showed how to compute them using Stata matrix commands and functions and on
Programming an estimation command in Stata: A first ado command, in which I introduced some ado-programming concepts. Although I introduce some local macro tricks that I use all the time, I also build on Programing an estimation command in Stata: Where to store your stuff.

This is the sixth 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…

\(\newcommand{\epsilonb}{\boldsymbol{\epsilon}}
\newcommand{\ebi}{\boldsymbol{\epsilon}_i}
\newcommand{\Sigmab}{\boldsymbol{\Sigma}}
\newcommand{\betab}{\boldsymbol{\beta}}
\newcommand{\eb}{{\bf e}}
\newcommand{\xb}{{\bf x}}
\newcommand{\zb}{{\bf z}}
\newcommand{\yb}{{\bf y}}
\newcommand{\Xb}{{\bf X}}
\newcommand{\Mb}{{\bf M}}
\newcommand{\Eb}{{\bf E}}
\newcommand{\Xtb}{\tilde{\bf X}}
\newcommand{\Vb}{{\bf V}}\)I present the formulas for computing the ordinary least-squares (OLS) estimator, and I discuss some do-file implementations of them. I discuss the formulas and the computation of independence-based standard errors, robust standard errors, and cluster-robust standard errors. I introduce the Stata matrix commands and matrix functions that I use in ado-commands that I discuss in upcoming posts.

This is the 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. Read more…

Random-effects and fixed-effects panel-data models do not allow me to use observable information of previous periods in my model. They are static. Dynamic panel-data models use current and past information. For instance, I may model current health outcomes as a function of health outcomes in the past— a sensible modeling assumption— and of past observable and unobservable characteristics.

Today I will provide information that will help you interpret the estimation and postestimation results from Stata’s Arellano–Bond estimator xtabond, the most common linear dynamic panel-data estimator. Read more…

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 ado-file programming and discuss two versions of the command. Along the way, I illustrate some of the postestimation features that work after the command.

This is the fourth 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 use features new to Stata 14.1 to estimate an average treatment effect (ATE) for a probit model with an endogenous treatment. In 14.1, we added new prediction statistics after mlexp that margins can use to estimate an ATE.

I am building on a previous post in which I demonstrated how to use mlexp to estimate the parameters of a probit model with sample selection. Our results match those obtained with biprobit; see [R] biprobit for more details. In a future post, I use these techniques to estimate treatment-effect parameters not yet available from another Stata command. Read more…

I discuss a pair of examples that illustrate the differences between global macros and local macros. You can view this post as a technical appendix to the previous post in the #StataProgramming series, which introduced global macros and local macros.

In every command I write, I use local macros to store stuff in a workspace that will not alter a user’s data and to make my code easier to read. A good understanding of the differences between global macros and local macros helps me to write better code. The essential differences between global macros and local macros can be summarized in two points. Read more…

Today I will discuss Mundlak’s (1978) alternative to the Hausman test. Unlike the latter, the Mundlak approach may be used when the errors are heteroskedastic or have intragroup correlation. Read more…

If you tell me “I program in Stata”, it makes me happy, but I do not know what you mean. Do you write scripts to make your research reproducible, or do you write Stata commands that anyone can use and reuse? In the series #StataProgramming, I will show you how to write your own commands, but I start at the beginning. Discussing the difference between scripts and commands here introduces some essential programming concepts and constructions that I use to write scripts and commands.

This is the 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. Read more…

Overview

In a previous post, David Drukker demonstrated how to use mlexp to estimate the degree of freedom parameter in a chi-squared distribution by maximum likelihood (ML). In this post, I am going to use mlexp to estimate the parameters of a probit model with sample selection. I will illustrate how to specify a more complex likelihood in mlexp and provide intuition for the probit model with sample selection. Our results match the heckprobit command; see [R] heckprobit for more details. Read more…