Predicting U.S. Business-Cycle Regimes
用逻辑分类方法识别和预测美国二战后商业周期的扩张与收缩阶段，基于NBER参考转折点，使用领先指标变量进行一个月和三个月的预测，比马尔可夫转换模型更准确。
This 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. We present a coherent theoretical framework for this task using measures of discriminatory information. The analysis encompasses model selection, parameter estimation, and classification decision rules. We examine the performance of logistic procedures in reproducing the NBER regime classifications and in predicting one and three months ahead using leading-indicator variables. Our models are shown to provide substantially more accurate business-cycle regime predictions than Markov switching specifications.