论文标题
从统计角度来看，经验宏观经济和DSGE建模
Empirical Macroeconomics and DSGE Modeling in Statistical Perspective
论文作者
论文摘要
数十年来，动态随机通用平衡（DSGE）模型一直是无处不在且有争议的宏观经济学的一部分。在本文中，我们仅作为统计模型将DSGES纯化。我们通过将两个通用模型验证检查应用于Canonical SMETS和WOUTERS 2007 DSGE来做到这一点：（1）我们模拟该模型，并从其自己的仿真输出中估算了如何估计它的能力，并且（2）我们看到它看起来对无义数据的符合程度。我们发现（1）即使有了数百年的数据，该模型的估计仍然很差，并且（2）当我们随机交换系列序列时，（例如）（例如）该模型随着通货膨胀率的真正工作时间而获得的东西，随着工作时间的实际工作是实际的投资等等，拟合通常只有略有障碍，并且在案例的很大一部分中，实际上会改善样品的比例。综上所述，这些发现对此DSGE的参数估计值以及该规范是否代表了经济的结构性的任何结构性的意义。从建设性上讲，我们的方法可用于使用宏观经济时间序列的任何人。
Dynamic stochastic general equilibrium (DSGE) models have been an ubiquitous, and controversial, part of macroeconomics for decades. In this paper, we approach DSGEs purely as statstical models. We do this by applying two common model validation checks to the canonical Smets and Wouters 2007 DSGE: (1) we simulate the model and see how well it can be estimated from its own simulation output, and (2) we see how well it can seem to fit nonsense data. We find that (1) even with centuries' worth of data, the model remains poorly estimated, and (2) when we swap series at random, so that (e.g.) what the model gets as the inflation rate is really hours worked, what it gets as hours worked is really investment, etc., the fit is often only slightly impaired, and in a large percentage of cases actually improves (even out of sample). Taken together, these findings cast serious doubt on the meaningfulness of parameter estimates for this DSGE, and on whether this specification represents anything structural about the economy. Constructively, our approaches can be used for model validation by anyone working with macroeconomic time series.
