标题： 使用结构模型、简化形式模型和神经网络模型通过合并国家数据提高宏观经济模型的有效性和预测性能 显示英文标题

标题： Improving Macroeconomic Model Validity and Forecasting Performance with Pooled Country Data using Structural, Reduced Form, and Neural Network Model

摘要： 我们表明，将国家按面板维度合并到宏观经济数据中，可以以统计上显著的幅度提高结构模型、简化形式模型和机器学习（ML）方法的泛化能力，从而产生最先进的结果。 使用在样本外测试集上评估的GDP预测，该过程在某些简化形式模型中，跨时间跨度和模型减少了12%的均方根误差，在动态结构一般均衡模型中，跨时间跨度减少了24%。 从训练集中移除美国数据并进行样本外的国家层面预测，我们表明当在合并数据上训练时，简化形式模型和结构模型更具政策不变性，并优于仅使用美国数据的基准模型。 鉴于ML模型在数据丰富的环境中的比较优势，我们证明我们的循环神经网络模型和自动化ML方法优于所有测试的基准经济模型。 稳健性检验表明，我们的优越表现是可重复的、数值稳定的，并且适用于各种模型。 显示英文摘要

摘要： We show that pooling countries across a panel dimension to macroeconomic data can improve by a statistically significant margin the generalization ability of structural, reduced form, and machine learning (ML) methods to produce state-of-the-art results. Using GDP forecasts evaluated on an out-of-sample test set, this procedure reduces root mean squared error by 12\% across horizons and models for certain reduced-form models and by 24\% across horizons for dynamic structural general equilibrium models. Removing US data from the training set and forecasting out-of-sample country-wise, we show that reduced-form and structural models are more policy-invariant when trained on pooled data, and outperform a baseline that uses US data only. Given the comparative advantage of ML models in a data-rich regime, we demonstrate that our recurrent neural network model and automated ML approach outperform all tested baseline economic models. Robustness checks indicate that our outperformance is reproducible, numerically stable, and generalizable across models.