The importance of nonlinearity in reproducing business cycle features
By: by James Morley and Jeremy M. Piger.
Contributors: Piger, Jeremy Max.
Series: Working paper ;; 2004-032A; Working paper (Federal Reserve Bank of St. Louis : Online) ;; 2004-032A.
Publisher: Federal Reserve Bank of St. Louis
Published: 2004
Place of publication: [St. Louis, Mo.]
Language: eng
Format: Electronic resource
Subjects: Business cycles -- Mathematical models.
Description: "This paper considers the ability of simulated data from linear and nonlinear time-series models to reproduce features in U.S. real GDP data related to business cycle phases.  We focus our analysis on a number of linear ARIMA models and nonlinear Markov-switching models.  To determine the timing of business cycle phases for the simulated data, we present a model-free algorithm that is more successful than previous methods at matching NBER dates in the postwar data.  We find that both linear and Markov-switching models are able to reproduce business cycle features such as the average growth rate in recessions, the average length of recessions, and the total number of recessions.  However, we find that Markov-switching models are better than linear models at reproducing the variability of growth rates in different business cycle phases.  Furthermore, certain Markov-switching specifications are able to reproduce high-growth recoveries following recessions and a strong correlation between the severity of a recession and the strength of the subsequent recovery.  Thus, we conclude that nonlinearity is important in reproducing business cycle features"--Federal Reserve Bank of St. Louis web site.
Notes: Also available in print.
Includes bibliographical references.
Title from PDF file as viewed on 11/22/2004.
System requirements: Adobe Acrobat Reader.
Mode of access: World Wide Web.