
TL;DR
The paper introduces Harmonic Synthetic Control (HSC), a novel method that improves counterfactual predictions in nonstationary macroeconomic data by combining donor matching with residual forecasting.
Contribution
HSC replaces binary filtering choices with a soft allocation mechanism, jointly estimating donor weights and a smooth residual component for better nonstationary data modeling.
Findings
HSC adapts across different regimes of stochastic trends.
Monte Carlo exercises demonstrate HSC's robustness and flexibility.
HSC interpolates between differenced and raw outcome synthetic controls.
Abstract
Synthetic control methods can produce misleading counterfactual predictions when outcome series contain unit-specific stochastic trends, a common feature of nonstationary macroeconomic data. Existing remedies, such as pre-filtering or differencing, reduce spurious matching but may discard shared nonstationary variation that helps estimate donor weights. We propose Harmonic Synthetic Control (HSC), which replaces this binary choice with a soft allocation mechanism. HSC jointly estimates donor weights and a treated-unit-specific smooth residual component, then extrapolates this component into post-treatment periods using a time-series forecaster. A tuning parameter, selected by rolling-origin cross-validation, governs the division between donor matching and forecasting. As it varies, HSC continuously interpolates between synthetic control applied to differenced outcomes and synthetic…
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