JBES: Regime Switching and Threshold Models

JBES: Regime Switching and Threshold Models

Kung-Sik Chan of the University of Iowa, Bruce E. Hansen of the University of Wisconsin-Madison, and Allan Timmermann of the University of California-San Diego

This special issue of the Journal of Business & Economic Statistics (April) on regime switching and threshold models is motivated by the mounting empirical evidence of important nonlinearities in regression models commonly used to model the dynamics in macroeconomic and financial time series. Commonly cited examples include the very different behavior of second moments for many macroeconomic time series before and after the Great Moderation in the early eighties, the different behavior of U.S. interest rates during the Federal Reserve’s Monetarist Experiment from 1979–1982, and the behavior of a variety of risk indicators during the more recent global financial crisis. These are episodes that can be difficult to model in the context of standard linear regression models.

The key difference between Markov switching models and threshold models is that the former assume that the underlying state process that gives rise to the nonlinear dynamics (regime switching) is latent, whereas threshold models commonly allow the nonlinear effect to be driven by observable variables but assume the number of thresholds and the threshold values to be unknown. However, it is often overlooked that the general formulation of the threshold model includes the Markov switching model. Thus, these two classes of models share many common features. From an econometric perspective, both classes of models are affected by the presence of unidentified parameters under the null, which poses challenges to inference, including the number of thresholds (or regimes) and their location. Empirically, both types of models can, by design, allow for discrete, nonlinear effects.

The papers brought together in this special issue highlight both similarities and differences for threshold- and regime-switching models, offering many novel insights along methodological, computational, and empirical lines.

Visit http://magazine.amstat.org/blog/2017/08/01/jbes-highlights ‎to read about the papers in this issue from the following authors:

  • Luc Bauwens
  • Biqing Cai
  • Laurent Callot
  • Mehmet Caner
  • Jean-Francois Carpantier
  • Ngai Hang Chan
  • Kung-Sik Chan
  • Arnaud Dufays
  • Steven Durlauf
  • Jiti Gao
  • Jesus Gonzalo
  • Bruce Hansen
  • Ching-Kang Ing
  • Young-Joo Kim
  • Anders Bredahl Koch
  • Andros Kourtellos
  • Quodong Li
  • Wai Keung Li
  • Yuanbo Li
  • Shiqing Ling
  • Zhao Liu
  • Davide Pettenuzzo
  • Jean-Yves Pitarakis
  • Juan Anders Riquelme
  • Myung Huan Seo
  • Chih Ming Tan
  • Allan Timmermann
  • Dag Tjostheim
  • Yaxing Yang
  • Chun Yip Yau
  • Qianqin Zhu