The question often arises as to whether one can estimate a transfer function model using Stata. While Stata does not currently have a convenience command for doing so, this article will demonstrate that estimating such a model can be accomplished quite easily using Stata's arima command. The...

This article demonstrates how to estimate the parameters of a system of seemingly unrelated regressions when the equations are unbalanced, i.e., when the equations have an unequal number of observations. With estimators that require the data to be in wide format, such as Stata's sureg, the...

Welcome to From the help desk. From the help desk is written by the people in Technical Services at StataCorp and deals with issues that they have found to be of concern to a large fraction of Stata users. It is the rare column in this series that deals with sophisticated programming issues...

This article demonstrates that, although there is no command in Stata for fitting hurdle models, the parameters of a hurdle model can be estimated in Stata rather easily using a combination of existing commands. We also include a likelihood evaluator to be used with Stata's ml facilities to...

Polynomial distributed lag models (PDLs) are finite-order distributed lag models with the impulse-response function constrained to lie on a polynomial of known degree. You can estimate the parameters of a PDL directly via constrained ordinary least squares, or you can derive a reduced form of...

In this presentation, I cover how to use the new factor variables features in Stata 11. Stata’s new factor variables notation allows you to identify categorical covariates as factor variables, provides a convenient notation for specifying indicator variables without having to generate them,...

In this presentation, I cover how to use Stata for survey data analysis assuming a fixed population. We will begin by reviewing the sampling methods used to collect survey data, and how they affect the estimation of totals, ratios, and regression coefficients. We will then cover the three...

This talk discusses Stata's features for analyzing survey data and correlated data, and will explain how and when to use the three major variance estimators for survey and correlated data: the linearization estimator, balanced repeated replications, and the clustered jackknife (the latter two...

Stata has a rich set of operators for specifying factor variables in linear and nonlinear regression models. I will show how to test for the effects of factor variables in these models. I will also show how to compare and contrast these effects using linear combinations of the model coefficients.

Introducing generalized SEM: (1) SEM with generalized linear response variables, and (2) SEM with multilevel mixed effects, whether linear or generalized linear. Generalized linear response variables mean you can now fit probit, logit, Poisson, multinomial logistic, ordered logit, ordered...