This session serves as an introduction to Stata 12’s new sem command for estimating the parameters of simultaneous-equations models. Some of the considered models include unobserved factors. Estimation methods include maximum likelihood and generalized method of moments.

<title>Abstract</title> One of the most widely used tests for spatial dependence is Moran's (1950) I test. The power of the test will depend on the extent to which the spatial-weights matrix employed in computing the Moran I test statistic properly specifies existing interaction links between spatial units....

This paper discusses the advantages of Halton sequences over pseudo- random uniform numbers when using simulation to approximate integrals numeri- cally. We describe two types of sequences and give Mata examples. Finally, we doc- ument the Mata function halton(), currently in release 9.1 of...

This article gives a brief overview of the popular methods for esti- mating variance components in linear models and describes several ways to obtain such estimates in Stata for various experimental designs. The article’s emphasis is on using xtmixed to estimate variance components. Prior to...

Stata 11 has new command gmm for estimating parameters by the generalized method of moments (GMM). gmm can estimate the parameters of linear and nonlinear models for cross-sectional, panel, and time-series data. In this presentation, I provide an introduction to GMM and to the gmm command.

Stata 11 has new commands sspace and dvech for estimating the parameters of space-space models and diagonal-vech multivariate GARCH models, respectively. In this presentation, I provide an introduction to space-space models, diagonal-vech multivariate GARCH models, the implemented estimators,...

After introducing time-series data management in Stata, the talk discusses estimation, inference, and interpretation of ARMA models, ARCH/GARCH models, VAR models, and SVAR models in Stata. The talk briefly introduces each model discussed.

This talk discusses estimation, inference, and interpretation of panel-data models using Stata. The talk usually covers the linear RE and FE models, linear RE and FE models with AR(1) errors, linear RE and FE models with general within-panel correlation structures, Hausman–Taylor estimation,...

After presenting a general introduction to the Mata matrix programming language, this talk discusses Mata’s many simple links to the Stata dataset and other important objects in Stata’s memory. An application to maximum simulated likelihood illustrates the programming techniques.