Leading Indicators of the Business Cycle: Dynamic Logit Models for OECD Countries and Russia

A. Pestova
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引用次数: 2

Abstract

In this paper, I develop the leading indicators of the business cycle turning points exploiting the quarterly panel dataset comprising OECD countries and Russia over the 1980-2013 period. Contrasting to the previous studies, I combine data on OECD countries and Russia into a single dataset and develop universal models suitable for the entire sample with a quality of predictions comparable to the analogues of single-country models. On the basis of conventional dynamic discrete dependent variable framework I estimate the business cycle leading indicator models at different forecasting horizons (from one to four quarters). The results demonstrate that there is a trade-off between forecasting accuracy and the earliness of the recession signal. Best predictions are achieved for the model with one quarter lag (approximately 94% of the observations were correctly classified with a noise-to-signal ratio of 7%). However, even the model with the four quarter lags correctly predicts more than 80% of recessions with the noise-to-signal ratio of 25% can be useful for the policy analysis. I also reveal significant gains of accounting for the credit market variables when forecasting recessions at the long horizons (four quarter lag) as their use leads to a significant reduction of the noise-to-signal ratio of the model. I propose using the “optimal” cut-off threshold of the binary models based on the minimization of regulator loss function arising from different types of wrong classification. I show that this optimal threshold improves model forecasts as compared to other exogenous thresholds.
经济周期的领先指标:经合组织国家和俄罗斯的动态Logit模型
在本文中,我利用由经合组织国家和俄罗斯在1980-2013年期间组成的季度面板数据集开发了商业周期转折点的领先指标。与之前的研究相比,我将经合组织国家和俄罗斯的数据合并成一个单一的数据集,并开发出适用于整个样本的通用模型,其预测质量可与单一国家模型的类似物相媲美。在传统的动态离散因变量框架的基础上,我估计了不同预测范围(从一个季度到四个季度)的商业周期领先指标模型。结果表明,在预测准确性和衰退信号的早期之间存在权衡。最好的预测是在四分之一滞后的模型中实现的(大约94%的观测值被正确分类,噪声-信号比为7%)。然而,即使具有四个季度滞后的模型正确预测了80%以上的衰退,噪声与信号比为25%,对政策分析也很有用。我还揭示了在预测长期衰退(四个季度滞后)时,考虑信贷市场变量的显著收益,因为它们的使用导致模型的噪信比显著降低。我建议使用基于最小化由不同类型的错误分类引起的调节器损失函数的二元模型的“最优”截止阈值。我表明,与其他外生阈值相比,这个最优阈值提高了模型预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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