An improved and extended Bayesian synthetic control model is presented, expanding upon the latent factor model in Tuomaala 2019. The changes we make include 1) standardization of the data prior to model fit - which improves efficiency and generalization across different data sets; 2) adding time varying covariates; 3) adding the ability to have multiple treated units; 4) fitting the latent factors within the Bayesian model; and, 5) a sparsity inducing prior to automatically tune the number of latent factors. We demonstrate the similarity of estimates to two traditional synthetic control studies in Abadie, Diamond, and Hainmueller 2010 and Abadie, Diamond, and Hainmueller 2015 and extend to multiple target series with a new example of estimating digital website visitation from changes in data collection due to digital privacy laws.
翻译:介绍了一个经过改进和扩展的巴耶斯合成控制模型,扩展了图奥马阿拉2019年的潜在要素模型。我们所作的修改包括:(1) 模型适用前数据的标准化----这提高了不同数据集的效率和普遍性;(2) 增加了时间差异的共变;(3) 增加了拥有多个处理单位的能力;(4) 将潜在因素与巴耶斯模式相匹配;(5) 在自动调节潜在因素数量之前,会引起一种偏狭现象。我们显示了与阿巴迪、钻石和海因穆勒2010年和阿巴迪、钻石和海因穆勒2015年两项传统合成控制研究的估计数相似性,并扩大到多个目标系列,并举了一个新例子,根据数字隐私法对数字网站访问的改变估算数字网站访问。