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.
Econometrica , volume=
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A new bootstrap goodness-of-fit test for the logistic propensity score model under nonignorable missing data, based on marginal sum-of-squared residuals, with asymptotic size and power guarantees.
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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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A goodness-of-fit test for the logistic propensity score model under nonignorable missing data
A new bootstrap goodness-of-fit test for the logistic propensity score model under nonignorable missing data, based on marginal sum-of-squared residuals, with asymptotic size and power guarantees.