使用结构模型、简化模型和神经网络模型改善宏观经济模型的有效性和预测性能
Improving Macroeconomic Model Validity and Forecasting Performance with Pooled Country Data using Structural, Reduced Form, and Neural Network Model
摘要：
我们发现，将国家汇集到宏观经济数据的面板维度上，可以通过统计上显著的幅度提高结构化，简化形式和机器学习（ML）方法的泛化能力，以产生最先进的结果。使用在样本外测试集上评估的GDP预测，对于某些简化形式的模型，该程序将跨视野和模型的均方根误差降低了12%，对于动态结构一般均衡模型，跨视野的均方根误差降低了24%。从训练集中删除美国数据并预测样本外国家，我们表明，在汇总数据上训练时，简化形式和结构模型更具政策不变性，并且表现优于仅使用美国数据的基线。鉴于ML模型在数据丰富的环境中的比较优势，我们证明了我们的递归神经网络模型和自动ML方法优于所有测试的基线经济模型。鲁棒性检查表明，我们的优异表现是可重复的，数值稳定，并可推广到不同的模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
