Predicting U.S. Business-Cycle Regimes
AbstractThis article examines the use of logistic classification methods for the identification and prediction of postwar U.S. business-cycle expansion and contraction regimes as defined by the National Bureau of Economic Research (NBER) reference turning-point dates. The authors present a coherent theoretical framework for this task using measures of discriminatory information. The analysis encompasses model selection, parameter estimation, and classification decision rules. The authors examine the performance of logistic procedures in reproducing the NBER regime classifications and in predicting one and three months ahead using leading-indicator variables. The authors' models are shown to provide substantially more accurate business-cycle regime predictions than Markov switching specifications. Coauthors are Hans Jessen, Denise R. Osborn, and Paul Simpson.
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Bibliographic InfoArticle provided by American Statistical Association in its journal Journal of Business and Economic Statistics.
Volume (Year): 17 (1999)
Issue (Month): 3 (July)
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Web page: http://www.amstat.org/publications/jbes/index.cfm?fuseaction=main
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