MF-SCM constructs synthetic control weights from mixed-frequency data, proves the estimator achieves the lowest possible squared prediction error among averaging methods, and derives asymptotic inference for the average treatment effect.
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SABRE is a simulation-based bias correction framework that reduces finite-sample bias for the parametric component and dispersion parameter in semiparametric regression models, with asymptotic bias reduction without variance inflation shown for generalized partially linear models.
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Synthetic Control Method with Mixed Frequency Data
MF-SCM constructs synthetic control weights from mixed-frequency data, proves the estimator achieves the lowest possible squared prediction error among averaging methods, and derives asymptotic inference for the average treatment effect.
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Bias Correction for Semiparametric Regression Models
SABRE is a simulation-based bias correction framework that reduces finite-sample bias for the parametric component and dispersion parameter in semiparametric regression models, with asymptotic bias reduction without variance inflation shown for generalized partially linear models.