标题： L2正则化用于经济预测 显示英文标题

标题： L2-relaxation for Economic Prediction

摘要： 我们利用许多回归器的集合进行经济预测，其数量可能超过样本量。 一个潜在的潜在因子结构意味着具有高度相关协变量的密集回归模型。 我们提出了L2松弛方法来估计回归系数并外推样本外（OOS）结果。 该框架可以应用于使用面板数据方法（PDA）的政策评估，在此我们进一步建立了平均处理效应的推断。 此外，我们将PDA中的传统单一单位设置扩展为允许在短期处理后期间有多个处理单位。 蒙特卡洛模拟表明，我们的方法在样本外预测和政策评估方面表现出色。 我们通过两个实证例子说明了我们的方法： (i) 预测中国的生产者价格指数增长率并评估房地产调控的影响，以及(ii) 估计英国脱欧对英国和欧洲公司股票收益的影响。 显示英文摘要

摘要： We leverage an ensemble of many regressors, the number of which can exceed the sample size, for economic prediction. An underlying latent factor structure implies a dense regression model with highly correlated covariates. We propose the L2-relaxation method for estimating the regression coefficients and extrapolating the out-of-sample (OOS) outcomes. This framework can be applied to policy evaluation using the panel data approach (PDA), where we further establish inference for the average treatment effect. In addition, we extend the traditional single unit setting in PDA to allow for many treated units with a short post-treatment period. Monte Carlo simulations demonstrate that our approach exhibits excellent finite sample performance for both OOS prediction and policy evaluation. We illustrate our method with two empirical examples: (i) predicting China's producer price index growth rate and evaluating the effect of real estate regulations, and (ii) estimating the impact of Brexit on the stock returns of British and European companies.