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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A Neyman-orthogonal moment estimator with adjusted nonparametric fixed effects achieves root-NT asymptotic normality for common parameters in two-way heterogeneous panel models.
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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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Inference on Linear Regressions with Two-Way Unobserved Heterogeneity
A Neyman-orthogonal moment estimator with adjusted nonparametric fixed effects achieves root-NT asymptotic normality for common parameters in two-way heterogeneous panel models.