Thanks to Kit Baum, the first official release of xtdpdml is now available on SSC. Anyone who has installed an earlier beta version will want to uninstall it first and replace it with the SSC version.
Overview. Paul Allison, Enrique Moral-Benito, and Richard Williams are currently working on a project entitled "Dynamic Panel Data Modeling using Maximum Likelihood." Panel data have many advantages when trying to make causal inferences but can also be difficult to work with. We show that ML provides an alternative to widely used GMM methods such as Arellano-Bond and is superior in many cases. We have prepared a Stata command called xtdpdml that greatly simplifies the process of estimating our models.
Description. Panel data make it possible both to control for unobserved confounders and to include lagged, endogenous regressors. Trying to do both at the same time, however, leads to serious estimation difficulties. In the econometric literature, these problems have been solved by using lagged instrumental variables together with the generalized method of moments (GMM). In Stata, commands such as xtabond and xtdpdsys have been used for these models. xtdpdml addresses the same problems via maximum likelihood estimation implemented with Stata's structural equation modeling (sem) command. The ML (sem) method is substantially more efficient than the GMM method when the normality assumption is met and suffers less from finite sample biases. xtdpdml greatly simplifies the SEM model specification process; makes it possible to test and relax many of the constraints that are typically embodied in dynamic panel models; unlike most related methods, allows for the inclusion of time-invariant variables in the model; takes advantage of Stata's ability to use full information maximum likelihood (FIML) for dealing with missing data; provides an overall goodness of fit measure by default and provides easy access to others; and can also generate code for use with Mplus.
For more information. The support page for xtdpdml can be found at
http://www3.nd.edu/~rwilliam/dynamic/index.html
It includes works in progress, suggested readings, and replicable examples. Many of the examples and readings are targeted at Economists. However, other social scientists may be especially interested in the examples where we show how models suggested by sociologists Kenneth Bollen and Jennie Brand for panel models with random and fixed effects can be easily estimated with xtdpdml.
Any comments on the program or its documentation are welcome. Contact information is included in the help file and on the support page.
Overview. Paul Allison, Enrique Moral-Benito, and Richard Williams are currently working on a project entitled "Dynamic Panel Data Modeling using Maximum Likelihood." Panel data have many advantages when trying to make causal inferences but can also be difficult to work with. We show that ML provides an alternative to widely used GMM methods such as Arellano-Bond and is superior in many cases. We have prepared a Stata command called xtdpdml that greatly simplifies the process of estimating our models.
Description. Panel data make it possible both to control for unobserved confounders and to include lagged, endogenous regressors. Trying to do both at the same time, however, leads to serious estimation difficulties. In the econometric literature, these problems have been solved by using lagged instrumental variables together with the generalized method of moments (GMM). In Stata, commands such as xtabond and xtdpdsys have been used for these models. xtdpdml addresses the same problems via maximum likelihood estimation implemented with Stata's structural equation modeling (sem) command. The ML (sem) method is substantially more efficient than the GMM method when the normality assumption is met and suffers less from finite sample biases. xtdpdml greatly simplifies the SEM model specification process; makes it possible to test and relax many of the constraints that are typically embodied in dynamic panel models; unlike most related methods, allows for the inclusion of time-invariant variables in the model; takes advantage of Stata's ability to use full information maximum likelihood (FIML) for dealing with missing data; provides an overall goodness of fit measure by default and provides easy access to others; and can also generate code for use with Mplus.
For more information. The support page for xtdpdml can be found at
http://www3.nd.edu/~rwilliam/dynamic/index.html
It includes works in progress, suggested readings, and replicable examples. Many of the examples and readings are targeted at Economists. However, other social scientists may be especially interested in the examples where we show how models suggested by sociologists Kenneth Bollen and Jennie Brand for panel models with random and fixed effects can be easily estimated with xtdpdml.
Any comments on the program or its documentation are welcome. Contact information is included in the help file and on the support page.
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