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Bayesian Probability Forecast of a 2007-2008 Economic Recession in the U.S.

This paper presents a Bayesian methodology for estimating probability of a downturn in the economy and applies it to the 2007-2008 state of the U.S. economy with the focus on investigating the occurrence of a recession. In the methodological development, information theory (Kullback and Shannon) is used to assess the change in the amount of information when the economy evolves in moving from one state to another. For the application purposes, the latest information (March 2008) of the Conference Board's Composite Leading Economic Indicators (CLI) and Composite Leading Economic Indicators Diffusion Index (CLID) are used in estimating Bayesian probability of a downturn in U.S. The analysis shows that the models developed have been very accurate in predicting all the past recessions since 1959 correctly with only one false alarm in mid-1960s. When CLID is used as prior information for CLI as evidence, it helped the Bayesian probability of a downturn to be more decisive in its signaling, but at a cost of more false alarms. Overall, the models using only CLI have the best performance in terms of accuracy and false alarm. The best models have signaled for a downturn in the U.S. Economy as early as the Fall 2007.