Introduction

Zellner, Hong, and Gulati (1990) and Zellner and Hong (1989) formulated the problem of forecasting turning points in economic time series using a Bayesian decision theoretic framework. The methodology was … applied by Zellner, Hong, and Min (1990) (hereafter ZHM) to a host of models to forecast turning points in the international growth rates of real output for eighteen countries over the period 1974–86. They compared the performance of fixed parameter autoregressive leading indicator models (FP/ARLI), time-varying parameter autoregressive leading indicator models (TVP/ARLI), exponentially weighted autoregressive leading indicator models (EW/ARLI), and a version of each of these models that includes a world income variable – FP/ARLI/WI, TVP/ARLI/WI, EW/ARLI/WI. In addition, they implemented a pooling scheme for each of the models. A similar host of models is analysed here in order to assess whether these techniques hold promise for forecasting turning points in regional labor markets.

The innovative aspect of the ZHM study is not the models employed, but the use of the observations along with an explicit definition of a turning point, either a downturn (DT) or upturn (UT). This allows for a Bayesian computation of probabilities of a DT or UT given the past data from a model's predictive probability density function (pdf) for future observations. After computing these probabilities from the data, they can be used in a decision theoretic framework along with a loss structure in order to produce an optimal turning point forecast.