Develops a sieve-based local projection estimator that recovers causal state-dependent impulse responses under linearity of conditional means in micro-macro panels, with valid inference.
Econometrica , volume=
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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.
MATE is a missingness-adaptive thresholding estimator that consistently identifies the number of identifiable factors in high-dimensional incomplete data without imputation.
citing papers explorer
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Causal State-Dependent Local Projections
Develops a sieve-based local projection estimator that recovers causal state-dependent impulse responses under linearity of conditional means in micro-macro panels, with valid inference.
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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.
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Missingness-Adaptive Factor Identification in High-Dimensional Data
MATE is a missingness-adaptive thresholding estimator that consistently identifies the number of identifiable factors in high-dimensional incomplete data without imputation.