Pre-recession pattern of six economic indicators in the USA

Abstract

This paper applies a tightly parameterized pattern recognition algorithm, previously applied to earthquake prediction, to the problem of predicting recessions. Monthly data from 1962 to 1996 on six leading and coincident economic indicators for the USA are used. In the full sample, the model performs better than benchmark linear and non-linear models with the same number of parameters. Subsample and recursive analysis indicates that the algorithm is stable and produces reasonably accurate forecasts even when estimated using a small number of recessions.

Statistical properties of the cluster dynamics of the systems of statistical mechanics

“…Below we briefly discuss cluster dynamics in seismicity. More detailed discussion and further examples, suggesting universality of that phenomenon, are given in [KSS+,KS1,KS2].…”

Extreme events: dynamics, statistics and prediction

“…It is clear that this approach does not address in any way the issues of causality, and hence of remediation, only those of prediction and law enforcement. The pattern-recognition approach was thus extended, based on the above reasoning, to the start of economic recessions (Keilis-Borok et al, 2000), to episodes of sharp increase in the unemployment rate, called fast acceleration of unemployment (FAU; Keilis-Borok et al, 2005), and to homicide surges in megacities (Keilis-Borok et al, 2005). Based on these applications to complex economic and social systems, we try to formulate here a universal algorithm that is applied to monthly series of several relevant indices of system activity, including the appropriate definition of parameter values for the prediction of the extreme events of interest in the given system.…”

A classifying procedure for signalling turning points

“…As recently pointed out by Keilis-Borok et al (2000), recessions are rare, non-linear and complicated events. The chance of success in forecasting turning points is greatest if the method concentrates on this purpose only and uses all available information on turning points.…”