Business Cycle Accounting with Non-linear Solution Methods

这个项目的主要目的是评估线性和非线性求解方法的计算效率和精度之间的权衡。我们之所以会分析新冠肺炎危机，是因为经济波动幅度很大，这会导致线性方法的逼近误差很大。我们使用了Chari，Kehoe和McGritan(2007)的经济周期会计框架，因为它是一种有用的方法来将经济波动分解为具有代表性的来源。然而，由于计算量大，用非线性方法求解这一模型具有一定的挑战性。因此，这个项目不仅将为最近一次经济危机的根源提供新的见解，而且还将在计算效率和准确性之间进行权衡。在第一年，我们回顾了有关解决方法的文献，以设计我们的商业周期会计方法。特别是，联合调查员回顾了有限元方法和参数化期望算法。首席研究员回顾了有关人工神经网络的机器学习文献。在概念层面上，我们共同设计了基于人工神经网络的经济周期核算非线性求解方法。

The main aim of this project is to assess the tradeoff between computational efficiency and accuracy across linear and non-linear solution methods. We will analyze the Covid-19 crisis because economic fluctuations are large, which will lead to significant approximation errors in the linear method. We apply the Business Cycle Accounting framework of Chari, Kehoe and McGrattan (2007) because it is a useful method to decomposes economic fluctuations into representative sources. However, it is challenging to solve this model with non-linear methods due to the computational burden. Therefore, this project will provide new insights not only on the sources of the latest economic crisis, but also tradeoff between computational efficiency and accuracy.In the first year, we reviewed the literature on solution methods to design our Business Cycle Accounting method. In particular, the Co-Investigator reviewed the finite element method and the parameterized expectation algorithm. The Principal Investigator reviewed the machine learning literature on Artificial Neural Networks. We have jointly designed the non-linear solution method for Business Cycle Accounting with Artificial Neural Networks at a conceptual level.