Forecasting Asymmetric Unemployment Rates

Abstract

Asymmetric behavior has been documented in postwar quarterly U.S. unemployment rates. This suggests that improvement over conventional linear forecasts may be possible through the use of nonlinear time-series models. In this note an out-of-sample forecasting competition is carried out for a set of leading nonlinear time-series models. It is shown that several nonlinear forecasts do indeed dominate the linear forecast. The results are sensitive, however, to whether a stationarity-inducing transformation is applied to the nonstationary unemployment rate series. © 1998 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology

The brevity and violence of contractions and expansions

“…Beaudry and Koop (1993), Hussey (1992), Hess and Iwata (1997), Montgomery et al (1998), Rothman (1998), and Koop and Potter (1999) use other non-linear models to look for business cycle asymmetries. Clements and Krolzig (2003) bridge the two …rst branches of the literature, using a regime-switching model to look for skewness.…”

Inflation and Unemployment Forecasting with Genetic Support Vector Regression

“…Forecasting unemployment rates is also a well documented case study in the literature (Rothman (1998), Montgomery et al (1998)). Swanson and White (1998) forecast several macroeconomic time series, including US unemployment, with linear models and Neural Networks (NNs).…”

Comparing smooth transition and Markov switching autoregressive models of US unemployment

“…This may be partly due to our use of a monthly data frequency, rather than the quarterly frequency in, for example, Rothman (1998) and Montgomery et al (1998). Indeed, in another context, Klaassen (2005) finds that using lower-frequency data can mask evidence in favor of regime switching.…”

“…In the recent literature, several studies (Rothman, 1998;Montgomery et al, 1998;Koop and Potter, 1999;Van Dijk et al, 2002) have attempted to capture this salient feature by means of well-known nonlinear models such as threshold autoregression (TAR), the closely related logistic smooth transition autoregression (LSTAR), and Markov switching autoregression (MSAR). The first two papers attempt to base model choice on a comparison of forecasting performance.…”