ArCo: an artificial counterfactual approach for high-dimensional panel time-series data

DOI10.1016/j.jeconom.2018.07.005zbMath1452.62891OpenAlexW3123012394MaRDI QIDQ1739593

Xianqiang Yang

Publication date: 26 April 2019

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10438/18334

zbMATH Keywords

factor modelsstructural breakLassopolicy evaluationcomparative studiescounterfactual analysismultivariate treatment effectssynthetic control

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Applications of statistics to economics (62P20) Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items

On the Properties of the Synthetic Control Estimator with Many Periods and Many Controls ⋮ Counterfactual Analysis With Artificial Controls: Inference, High Dimensions, and Nonstationarity ⋮ An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls ⋮ Do We Exploit all Information for Counterfactual Analysis? Benefits of Factor Models and Idiosyncratic Correction ⋮ Recent advances in statistical methodologies in evaluating program for high-dimensional data ⋮ Forward-selected panel data approach for program evaluation ⋮ Synthetic learner: model-free inference on treatments over time ⋮ Bridging factor and sparse models ⋮ Synthetic Control with Time Varying Coefficients A State Space Approach with Bayesian Shrinkage ⋮ Confidence intervals of treatment effects in panel data models with interactive fixed effects ⋮ Assessing the causal effect of binary interventions from observational panel data with few treated units ⋮ ArCo

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