#2004-032B "The Importance of Nonlinearity in Reproducing Business Cycle Features"
by James Morley, and Jeremy M. Piger
November 2004
Revised May 2005

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. More...

PUBLISHED: in C. Milas, P. Rothman, and D. van Dijk (eds.), Nonlinear Time Series Analysis of Business Cycles, Elsevier Science, Amsterdam, 2006

#2004-014D "A Bayesian Approach to Counterfactual Analysis of Structural Change"
by Chang-Jin Kim, James Morley, and Jeremy M. Piger
July 2004
Revised June 2006

In this paper, we develop a Bayesian approach to counterfactual analysis of structural change. Contrary to previous analysis based on classical point estimates, this approach provides a straightforward measure of estimation uncertainty for the counterfactual quantity of interest. More...

#2004-006D "A Steady-State Approach to Trend/Cycle Decomposition of Regime-Switching Processes"
by James Morley, and Jeremy M. Piger
March 2004
Revised June 2005

In this paper, we present a new approach to trend/cycle decomposition under the assumption that the trend is the permanent component and the cycle is the transitory component of an integrated time series. The permanent component is defined as the steady-state level of the series, a definition that has exploitable forecasting implications useful for identification. We operationalize the steady-state approach for regime- switching processes and we use generated data from such processes to demonstrate the advantages of the steady-state approach over alternative approaches to trend/cycle decomposition. More...

#2002-014E "Nonlinearity and the Permanent Effects of Recessions"
by Chang-Jin Kim, James Morley, and Jeremy M. Piger
October 2002
Revised December 2003

This paper presents a new nonlinear time series model that captures a post-recession "bounce-back" in the level of aggregate output. While a number of studies have examined this type of business cycle asymmetry using recession-based dummy variables and threshold models, we relate the "bounce-back" effect to an endogenously estimated unobservable Markov-switching state variable. When the model is applied to U.S. real GDP, we find that the Markov-switching regimes are closely related to NBER-dated recessions and expansions. More...

PUBLISHED: Journal of Applied Econometrics, 2005, 20(2), pp. 291-309