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Composite Likelihood Estimation of an Autoregressive Panel Probit Model with Random Effects

Staff working paper 2019-16 Kerem Tuzcuoglu
Modeling and estimating persistent discrete data can be challenging. In this paper, we use an autoregressive panel probit model where the autocorrelation in the discrete variable is driven by the autocorrelation in the latent variable. In such a non-linear model, the autocorrelation in an unobserved variable results in an intractable likelihood containing high-dimensional integrals.
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