Programming an estimation command in Stata: A map to posted entries (up to date 23 February 2018)

That can assist you write Stata instructions that folks need to use, I illustrate how Stata syntax is predictable and provides an outline of the estimation-postestimation construction that you’ll want to emulate in your applications.

I focus on the distinction between scripts and instructions, and I introduce some important programming ideas and constructions that I exploit to jot down the scripts and instructions.

I focus on a pair of examples that illustrate the variations between world macros and native macros.

I focus on the code for a easy estimation command to concentrate on the small print of implement an estimation command. The command that I focus on estimates the imply by the pattern common. I start by reviewing the formulation and a do-file that implements them. I subsequently introduce ado-file programming and focus on two variations of the command. Alongside the best way, I illustrate a number of the postestimation options that work after the command.

I current the formulation for computing the peculiar least-squares (OLS) estimator, and I focus on some do-file implementations of them. I focus on the formulation and the computation of independence-based normal errors, strong normal errors, and cluster–strong normal errors. I introduce the Stata matrix instructions and matrix features that I exploit in ado-commands that I focus on in upcoming posts.

I present write a Stata estimation command that implements the OLS estimator by explaining the code.

I exploit the syntax command to enhance the command that implements the OLS estimator that I mentioned in Programming an estimation command in Stata: A primary command for OLS. I present require that each one variables be numeric variables and make the command settle for time-series operated variables.

I modify the OLS command mentioned in Programming an estimation command in Stata: A greater OLS command to permit for pattern restrictions, to deal with lacking values, to permit for issue variables, and to take care of completely collinear variables.

I make three enhancements to the command that implements the OLS estimator that I mentioned in Programming an estimation command in Stata: Permitting for pattern restrictions and issue variables. First, I permit the person to request a strong estimator of the variance–covariance of the estimator. Second, I permit the person to suppress the fixed time period. Third, I retailer the residual levels of freedom in e(df_r) in order that take a look at will use the t or F distribution as an alternative of the traditional or chi-squared distribution to compute the p-value of Wald checks.

I make two enhancements to the command that implements the OLS estimator that I mentioned in Programming an estimation command in Stata: Permitting for choices. First, I add an choice for a cluster–strong estimator of the variance–covariance of the estimator (VCE). Second, I make the command settle for the fashionable syntax for both a strong or a cluster–strong estimator of the VCE. Within the course of, I exploit subroutines in my ado-program to facilitate the parsing, and I focus on some superior parsing methods.

I introduce Mata, the matrix programming language that’s a part of Stata.

I present write a perform in Mata, the matrix programming language that’s a part of Stata.

I focus on a sequence of ado-commands that use Mata to estimate the imply of a variable. The instructions illustrate a common construction for Stata-Mata applications.

I current the formulation for computing the OLS estimator and present compute them in Mata. This submit is a Mata model of Programming an estimation command in Stata: Utilizing Stata matrix instructions and features to compute OLS objects. I focus on the formulation and the computation of independence-based normal errors, strong normal errors, and cluster–strong normal errors.

I focus on a command that computes OLS leads to Mata, paying particular consideration to the construction of Stata applications that use Mata work features.

I present use the undocumented command _vce_parse to parse the choices for strong or cluster–strong estimators of the VCE. I then focus on myregress12.ado, which performs its computations in Mata and computes an IID-based, a strong, or a cluster–strong estimator of the VCE.

I evaluation the idea behind nonlinear optimization and get some follow in Mata programming by implementing an optimizer in Mata. This submit is designed that will help you develop your Mata programming abilities and to enhance your understanding of how the Mata optimization suites optimize() and moptimize() work.

I present use optimize() in Mata to maximise a Poisson log-likelihood perform and to acquire estimators of the VCE primarily based on IID observations or on strong strategies.

I focus on mypoisson1, which computes Poisson-regression leads to Mata. The code in mypoisson1.ado is remarkably much like the code in myregress11.ado, which computes OLS leads to Mata, as I mentioned in Programming an estimation command in Stata: An OLS command utilizing Mata.

I focus on a technique for dealing with issue variables when performing nonlinear optimization utilizing optimize(). After illustrating the difficulty attributable to issue variables, I current a technique and apply it to an instance utilizing optimize().

mypoisson2.ado handles issue variables and computes its Poisson–regression leads to Mata. I focus on the code for mypoisson2.ado, which I obtained by including the strategy for dealing with issue variables mentioned in Programming an estimation command in Stata: Dealing with issue variables in optimize() to mypoisson1.ado, mentioned in Programming an estimation command in Stata: A poisson command utilizing Mata.

mypoisson3.ado provides choices for a strong or a cluster–strong estimator of the variance–covariance of the estimator (VCE) to mypoisson2.ado, which I mentioned in Programming an estimation command in Stata: Dealing with issue variables in a poisson command utilizing Mata. mypoisson3.ado parses the vce() choice utilizing the strategies I mentioned in Programming an estimation command in Stata: Including strong and cluster–strong VCEs to our Mata-based OLS command. I present use optimize() to compute the strong or cluster–strong VCE.

Utilizing analytically computed derivatives can enormously cut back the time required to unravel a nonlinear estimation drawback. I present use analytically computed derivatives with optimize(), and I focus on mypoisson4.ado, which makes use of these analytically computed derivatives. Just a few strains of mypoisson4.ado differ from the code for mypoisson3.ado, which I mentioned in Programming an estimation command in Stata: Permitting for strong or cluster–strong normal errors in a poisson command utilizing Mata.

I make predict work after mypoisson5 by writing an ado-command that computes the predictions and by having mypoisson5 retailer the identify of this new ado-command in e(predict).

Earlier than you employ or distribute your estimation command, you need to confirm that it produces right outcomes and write a do-file that certifies that it does so. I focus on the processes of verifying and certifying an estimation command, and I current some strategies for writing a do-file that certifies mypoisson5, which I mentioned in earlier posts.

I need to write ado-commands to estimate the parameters of an exponential conditional imply (ECM) mannequin and probit conditional imply (PCM) mannequin by nonlinear least squares (NLS). Earlier than I can write these instructions, I would like to point out trick optimize() into performing the Gauss–Newton algorithm and apply this trick to those two issues.

I write ado-commands that estimate the parameters of an exponential conditional imply mannequin and a probit conditional imply mannequin by nonlinear least squares, utilizing the strategies that I mentioned within the submit Programming an estimation command in Stata: Nonlinear least-squares estimators. These instructions will both share a lot of code or repeat a lot of code, as a result of they’re so related. It’s virtually at all times higher to share code than to repeat code. Shared code solely must be modified in a single place so as to add a characteristic or to repair an issue; repeated code should be modified in every single place. I introduce Mata libraries to share Mata features throughout ado-commands, and I introduce wrapper instructions to share ado-code.

estat instructions show statistics after estimation. Many of those statistics are diagnostics or checks used to guage mannequin specification. Some statistics can be found in spite of everything estimation instructions; others are command particular. I illustrate how estat instructions work after which present write a command-specific estat command for the mypoisson command that I’ve been creating.

Writing a perform in one other language (like C, C++, or Java) that Stata calls is named writing a plugin for Stata or as writing a dynamic-link library (DLL) for Stata. On this submit, I focus on the tradeoffs of writing a plugin/DLL, and I focus on a easy program whose calculations I’ll substitute with plugins in subsequent posts.

Writing a perform in one other language (like C, C++, or Java) that Stata calls is named writing a plugin for Stata or as writing a dynamic-link library (DLL) for Stata. On this submit, I write a plugin in C that implements the calculations carried out by mymean_work() in mymean11.ado, mentioned in Programming an estimation command in Stata: Making ready to jot down a plugin.

A perform written in one other language (like C, C++, or Java) that Stata calls is named a plugin for Stata or as a dynamic-link library (DLL) for Stata. On this submit, I write a plugin in C++ that implements the calculations carried out by mymean_work() in mymean11.ado, mentioned in Programming an estimation command in Stata: Making ready to jot down a plugin.

A perform written in one other language (like C, C++, or Java) that Stata calls is named a plugin for Stata or as a dynamic-link library (DLL) for Stata. On this submit, I write a plugin in Java that implements the calculations carried out by mymean_work() in mymean11.ado, mentioned in Programming an estimation command in Stata: Making ready to jot down a plugin.