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=
2 Pith papers cite this work. Polarity classification is still indexing.
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MATE is a missingness-adaptive thresholding estimator that consistently identifies the number of identifiable factors in high-dimensional incomplete data without imputation.
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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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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.