Multilevel Modeling aML product info

A Multiprocess ExampleWhile there are several software packages on the market which support multilevel modeling, none offers aML's capabilities to mix outcome types in multiprocess (multiequation) settings. Consider a simple Heckman selection model. The continuous outcome of interest is modeled by a linear model:The model equations are specified in an intuitive manner following their mathematical representations. Residuals u and v are correlated across equations because they are defined as part of the same distribution and have the same "draw."define regressor set AlphaX; var = <list of variables>; define regressor set BetaX; var = <list of variables>; define normal distribution; dim=2; name=u; name=v; probit model; outcome = z; model = regressor set AlphaX + residual(draw=1, ref=u); continuous model; keep if (z>0); outcome = y; model = regressor set BetaX + residual(draw=1, ref=v); The example readily generalizes to other types of outcomes and to multilevel models. For example, you may estimate Heckmantype probit selection models ("heckprob" models), multilevel Heckman selection models, and further embed such models into a larger system of equations. Similarly, multivariate heterogeneity may be correlated across equations.
To download page 
This page was last updated on 9 January 2006 Send comments to webmaster@appliedml.com.