Robust PCA Synthetic Control

TL;DR

This paper introduces a new five-step robust PCA synthetic control algorithm that improves upon existing methods in comparative case studies, demonstrating better performance and robustness in placebo tests.

Contribution

The paper presents a novel five-step algorithm for robust PCA synthetic control, enhancing robustness and performance over previous models in comparative studies.

Findings

Outperforms the robust synthetic control model in placebo tests

Less sensitive to weights of synthetic members than Abadie et al. 2015

Demonstrates effectiveness in estimating counterfactual GDP scenarios

Abstract

In this study, I propose a five-step algorithm for synthetic control method for comparative studies. My algorithm builds on the synthetic control model of Abadie et al., 2015 and the later model of Amjad et al., 2018. I apply all three methods (robust PCA synthetic control, synthetic control, and robust synthetic control) to answer the hypothetical question, what would have been the per capita GDP of West Germany if it had not reunified with East Germany in 1990? I then apply all three algorithms in two placebo studies. Finally, I check for robustness. This paper demonstrates that my method can outperform the robust synthetic control model of Amjad et al., 2018 in placebo studies and is less sensitive to the weights of synthetic members than the model of Abadie et al., 2015.