In this talk, I introduce 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 the generalized method of moments.

In this talk, I introduce new methods in Stata 12 for filtering and decomposing time series and I show how to implement them. I provide an underlying framework for understanding and comparing the different methods. I also present a framework for interpreting the parameters.

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.

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.