TY - JOUR
ID - st0523
A1 - Williams, R.
A1 - Allison, P. D.
A1 - Moral-Benito, E.
TI - Linear dynamic panel-data estimation using maximum likelihood and structural equation modeling
JF - Stata Journal
PB - Stata Press
CY - College Station, TX
Y1 - 2018
VL - 18
IS - 2
SP - 293
EP - 326
KW - xtdpdml
KW - linear dynamic panel-data
KW - structural equation modeling
KW - maximum likelihood
UR - http://www.stata-journal.com/article.html?article=st0523
AB - Panel data make it possible both to control for unobserved confounders
and to include lagged, endogenous regressors. However, trying to do both simultaneously
leads to serious estimation difficulties. In the econometric literature, these
problems have been addressed by using lagged instrumental variables together
with the generalized method of moments, while in sociology the same problems
have been dealt with using maximum likelihood estimation and structural equation
modeling. While both approaches have merit, we show that the maximum
likelihoodâ€“structural equation models method is substantially more efficient than
the generalized method of moments method when the normality assumption is
met and that the former also suffers less from finite sample biases. We introduce
the command xtdpdml, which has syntax similar to other Stata commands for
linear dynamic panel-data estimation. xtdpdml greatly simplifies the structural
equation model specification process; makes it possible to test and relax many of
the constraints that are typically embodied in dynamic panel models; allows one
to include time-invariant variables in the model, unlike most related methods; and
takes advantage of Stataâ€™s ability to use full-information maximum likelihood for
dealing with missing data. The strengths and advantages of xtdpdml are illustrated
via examples from both economics and sociology.
ER -