Introduction

In previous work (Zellner, Hong, and Gulati 1990 and Zellner and Hong 1989), the problem of forecasting turning points in economic time series was formulated and solved in a Bayesian decision theoretic framework. The methodology was applied using a fixed parameter autoregressive, leading indicator (ARLI) model and unpooled data for eighteen countries to forecast turning points over the period 1974–85. In the present chapter, we investigate the extent to which use of exponential weighting, time-varying parameter ARLI models, and pooling techniques leads to improved results in forecasting turning points for the same eighteen countries over a slightly extended period, 1974–86.

The methodology employed in this work has benefited from earlier work of Wecker (1979), Moore and Zarnowitz (1982), Moore (1983), Zarnowitz (1985), and Kling (1987). Just as Wecker and Kling have done, we employ a model for the observations and an explicit definition of a turning point, for example a downturn (DT) or an upturn (UT). Along with Kling, we allow for parameter uncertainty by adopting a Bayesian approach and computing probabilities of a DT or UT given past data from a model's predictive probability density function (pdf) for future observations. Having computed such probabilities from the data, we use them in a decision theoretic framework with given loss structures to obtain optimal turning point forecasts which can readily be computed.

The plan of our chapter is as follows. In section 2, we explain our models and methods. Section 3 is devoted to a description of our data.