Title: Hamilton versus Hamilton: Spurious nonlinearities
Authors: Luiggi Donayre - University of Minnesota - Duluth (United States) [presenting]
Abstract: Using Monte Carlo simulations, the purpose is to evaluate the ability of the Hamilton Decomposition (HD) approach into trend and cycle to adequately identify asymmetries in business cycles fluctuations. By considering different specifications of linear and asymmetric processes consistent with previous estimates, the results indicate that the HD approach is unable to preserve true asymmetric behavior nor reproduce U.S. business cycles features, especially in highly persistent or mildly asymmetric processes, or in small samples. The findings are robust to the presence of a time-varying drift, the complexity of the autoregressive dynamics and symmetric nonlinearity. Furthermore, the HD approach generates spurious expansionary periods when none exist in the data-generating process. Interestingly, they occur, exclusively, in the case of Markov-switching models, but not for other nonlinear models. Meanwhile, the distortions are also present in the case of symmetric nonlinearity. Based on these findings, caution is called into question when the approach is applied to processes that are thought to behave nonlinearly.