AOI approximately inverts the likelihood mapping from fixed effects to outcomes to produce an estimator whose bias vanishes exponentially in T with double robustness.
arXiv preprint arXiv:2105.00879 , year=
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Derives sufficient statistics for feedback and heterogeneity in dynamic panel logit models, proves conditional likelihood identification is infeasible for Markov covariates, and proposes two assumptions to restore identification.
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Approximate Operator Inversion for Average Effects in Nonlinear Panel Models
AOI approximately inverts the likelihood mapping from fixed effects to outcomes to produce an estimator whose bias vanishes exponentially in T with double robustness.
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Sufficient Statistics for Markovian Feedback Processes and Unobserved Heterogeneity in Dynamic Panel Logit Models
Derives sufficient statistics for feedback and heterogeneity in dynamic panel logit models, proves conditional likelihood identification is infeasible for Markov covariates, and proposes two assumptions to restore identification.