论文标题
使用结构，简化形式和神经网络模型，通过合并的国家数据来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
论文作者
论文摘要
我们表明，跨面板维度汇集到宏观经济数据可以通过统计学意义的边缘来改善结构，简化形式和机器学习（ML）方法的概括能力，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，此过程将均方根误差降低到跨水平的12 \％，对于某些还原形式模型，对于动态结构一般平衡模型而言，对某些还原形式模型的模型以及24 \％的模型。从培训集中删除我们的数据并预测了乡村样本外的样本外，我们表明，在接受汇总数据的培训时，降低形式和结构模型更为不变，并且优于仅使用我们数据的基线。鉴于ML模型在数据丰富的制度中的比较优势，我们证明了我们的经常性神经网络模型和自动化ML方法的表现优于所有经过测试的基线经济模型。鲁棒性检查表明，我们的超越性能是可重现的，数值稳定的，并且可以在模型之间进行推广。
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.
