Predicting business cycle turning points with neural networks in an information-poor economy

Abstract

A feedforward neural network model is used to forecast turning points in the business cycle of postwar Lebanon. The NN has as inputs seven indicators of economic activity and as output the probability of a recession. The three-layered network is estimated using the back propagation algorithm. The NN is then used to forecast recursively a half-year ahead the probability of a recession in that period. The NN shows that two of the economic indicators can be used to construct a composite index of leading indicators that can be used to predict business cycles in the future.