Identifying business cycle turning points in real time with vector quantization

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• 作者：Andrea Giusto^a ; ^{andrea.giusto@dal.ca}Author Vitae ; Jeremy Piger^b ; ^{jpiger@uoregon.edu}Author Vitae
• 关键词：Classification ; Reference cycle ; Expansion ; Recession
• 刊名：International Journal of Forecasting
• 出版年：2017
• 出版时间：January-March 2017
• 年：2017
• 卷：33
• 期：1
• 页码：174-184
• 全文大小：470 K
• 卷排序：33

文摘

We propose a simple machine-learning algorithm known as Learning Vector Quantization (LVQ) for the purpose of identifying new U.S. business cycle turning points quickly in real time. LVQ is used widely for real-time statistical classification in many other fields, but has not previously been applied to the classification of economic variables, to the best of our knowledge. The algorithm is intuitive and simple to implement, and easily incorporates salient features of the real-time nowcasting environment, such as differences in data reporting lags across series. We evaluate the algorithm’s real-time ability to establish new business cycle turning points in the United States quickly and accurately over the past five NBER recessions. Despite its relative simplicity, the algorithm’s performance appears to be very competitive with those of commonly used alternatives.