Estimating Nonlinear Business Cycle Mechanisms with Linear Vector Autoregressions: A Monte Carlo Study*
通过蒙特卡洛模拟，研究线性向量自回归模型在识别非线性极限周期过程中的内生周期机制和周期频率方面的表现，发现线性VAR能定性识别周期机制，但定量上低估了交互机制的强度。
Abstract The paper investigates how well linear vector autoregressions (VARs) identify endogenous cycle mechanisms and cycle frequencies when the underlying process is a nonlinear limit cycle. We conduct Monte Carlo simulations with five nonlinear models in which cycles are driven by the interaction of two state variables. We find that while linear VARs quantitatively underestimate the strength of the interaction mechanism, they successfully identify the qualitative presence of a cycle mechanism in most cases (55%–100%). Our results further suggest that linear VARs are surprisingly successful at estimating cycle frequencies of nonlinear processes.