Synthetic Control with Time Varying Coefficients A State Space Approach with Bayesian Shrinkage

DOI10.1080/07350015.2022.2102025MaRDI QIDQ6190720

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Publication date: 6 March 2024

Published in: Journal of Business & Economic Statistics (Search for Journal in Brave)

zbMATH Keywords

state space modelingBayesian estimationcausal inferencesynthetic control

Mathematics Subject Classification ID

Applications of statistics to economics (62P20)

Cites Work

• Achieving shrinkage in a time-varying parameter model framework
• Inferring causal impact using Bayesian structural time-series models
• Simulation smoothing for state-space models: a computational efficiency analysis
• Estimation of average treatment effects with panel data: asymptotic theory and implementation
• ArCo: an artificial counterfactual approach for high-dimensional panel time-series data
• Stochastic model specification search for Gaussian and partial non-Gaussian state space models
• The Bayesian Lasso
• Hierarchical Shrinkage in Time‐Varying Parameter Models
• Statistical Inference for Average Treatment Effects Estimated by Synthetic Control Methods
• Counterfactual Analysis With Artificial Controls: Inference, High Dimensions, and Nonstationarity
• The Augmented Synthetic Control Method
• Prediction Intervals for Synthetic Control Methods
• Economic Predictions With Big Data: The Illusion of Sparsity
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