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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HSC jointly estimates donor weights and a treated-unit-specific smooth residual, then extrapolates the residual via a forecaster with a cross-validated tuning parameter that interpolates between differenced and raw synthetic control.
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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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The Harmonic Synthetic Control Method
HSC jointly estimates donor weights and a treated-unit-specific smooth residual, then extrapolates the residual via a forecaster with a cross-validated tuning parameter that interpolates between differenced and raw synthetic control.