mypoisson3.ado adds options for a robust or a cluster–robust estimator of the variance–covariance of the estimator (VCE) to mypoisson2.ado, which I discussed in Programming an estimation command in Stata: Handling factor variables in a poisson command using Mata. mypoisson3.ado parses the vce() option using the techniques I discussed in Programming an estimation command in Stata: Adding robust and cluster–robust VCEs to our Mata based OLS command. Below, I show how to use optimize() to compute the robust or cluster–robust VCE.

I only discuss what is new in the code for mypoisson3.ado, assuming that you are familiar with mypoisson2.ado.

This is the twenty-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.

A poisson command with options for a robust or a cluster–robust VCE

mypoisson3 computes Poisson-regression results in Mata. The syntax of the mypoisson3 command is

mypoisson3 depvar indepvars [if] [in] [, vce(robust | cluster clustervar) noconstant]

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

In the remainder of this post, I discuss Read more…

\(\newcommand{\epsb}{{\boldsymbol{\epsilon}}}
\newcommand{\mub}{{\boldsymbol{\mu}}}
\newcommand{\thetab}{{\boldsymbol{\theta}}}
\newcommand{\Thetab}{{\boldsymbol{\Theta}}}
\newcommand{\etab}{{\boldsymbol{\eta}}}
\newcommand{\Sigmab}{{\boldsymbol{\Sigma}}}
\newcommand{\Phib}{{\boldsymbol{\Phi}}}
\newcommand{\Phat}{\hat{{\bf P}}}\)Vector autoregression (VAR) is a useful tool for analyzing the dynamics of multiple time series. VAR expresses a vector of observed variables as a function of its own lags.

Simulation

Let’s begin by simulating a bivariate VAR(2) process using the following specification,

\[
\begin{bmatrix} y_{1,t}\\ y_{2,t}
\end{bmatrix}
= \mub + {\bf A}_1 \begin{bmatrix} y_{1,t-1}\\ y_{2,t-1}
\end{bmatrix} + {\bf A}_2 \begin{bmatrix} y_{1,t-2}\\ y_{2,t-2}
\end{bmatrix} + \epsb_t
\]

where \(y_{1,t}\) and \(y_{2,t}\) are the observed series at time \(t\), \(\mub\) is a \(2 \times 1\) vector of intercepts, \({\bf A}_1\) and \({\bf A}_2\) are \(2\times 2\) parameter matrices, and \(\epsb_t\) is a \(2\times 1\) vector of innovations that is uncorrelated over time. I assume a \(N({\bf 0},\Sigmab)\) distribution for the innovations \(\epsb_t\), where \(\Sigmab\) is a \(2\times 2\) covariance matrix.

I set my sample size to 1,100 and Read more…

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…

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…

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

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…

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