630 research outputs found
Fitting Structural Equation Models via Variational Approximations
Structural equation models are commonly used to capture the relationship
between sets of observed and unobservable variables. Traditionally these models
are fitted using frequentist approaches but recently researchers and
practitioners have developed increasing interest in Bayesian inference. In
Bayesian settings, inference for these models is typically performed via Markov
chain Monte Carlo methods, which may be computationally intensive for models
with a large number of manifest variables or complex structures. Variational
approximations can be a fast alternative; however, they have not been
adequately explored for this class of models. We develop a mean field
variational Bayes approach for fitting elemental structural equation models and
demonstrate how bootstrap can considerably improve the variational
approximation quality. We show that this variational approximation method can
provide reliable inference while being significantly faster than Markov chain
Monte Carlo
Identification and estimation of marginal effects in nonlinear panel models
This paper gives identification and estimation results for marginal effects in nonlinear panel models. We find that linear fixed effects estimators are not consistent, due in part to marginal effects not being identified. We derive bounds for marginal effects and show that they can tighten rapidly as the number of time series observations grows. We also show in numerical calculations that the bounds may be very tight for small numbers of observations, suggesting they may be useful in practice. We give an empirical illustration.
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