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Analysis and Development of Leading Indicators Using a Bayesian Turning-Points Approach
James P. LeSage
Journal of Business & Economic Statistics
Vol. 9, No. 3 (Jul., 1991), pp. 305-316
Stable URL: http://www.jstor.org/stable/1391295
Page Count: 12
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This article explores a Bayesian decision-theoretic approach for analysis and development of regional leading indicator models. The methods used here are derived from work by Zellner, Hong, and Gulati (1990) aimed at analyzing forecasts of turning points. Here, these methods are adapted so that they can be used to analyze existing regional leading-indicator series and to develop new improved versions of such series. The innovative aspect of this study is the use of the time series observations on the measure of economic activity that we wish to predict along with an explicit definition of a turning point, either a downturn or an upturn. We then establish a predictive relation between the composite indicator series and the variable measuring economic activity that allows a Bayesian computation of probabilities associated with the turning-point events. These probabilities are conditioned on the past data and the predictive pdf for future observations. Probabilities for the turning-point events make it quite clear how to interpret the information provided by period-to-period movements in the composite leading-indicator series. These methods can also be used to develop composite indicator series using the posterior probabilities from relations between individual indicator variables and the state of the economy as weights.
Journal of Business & Economic Statistics © 1991 American Statistical Association