Artificial Control Methods: A Monte Carlo Study

• Main Authors: Chih-Han Chen, 陳之翰
• Other Authors: Jau-Er Chen
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/80391696872803095612

碩士 === 國立臺灣大學 === 經濟學研究所 === 101 === The accuracy of the artificial control estimation using the panel-data counterfactual method proposed by Hsiao et al. (2012) and the synthetic control method proposed by Abadie et al. (2010) were evaluated using the Monte Carlo simulations. The aim was to determine which of the methods is superior in studies with time-variant treatment effect, individual treatment effect and data-driven subject selection process. A cross checking process and simulations conducted under various model settings provide guidance on the applicability of these two methods. Both methods perform satisfactory when the variation of common factors in time and factor-loadings across regions are small. In most cases the panel-data counterfactual method is more accurate in artificial control estimation in term of mean-square-deviation criteria than the synthetic control method. Though both methods must be used with caution, the panel-data counterfactual method is clearly the better method suggested by the Monte Carlo results.