Superior forecasts of the US unemployment rate using a nonparametric method

• 成果类型: Article
• 署名作者: Golan, A; Perloff, JM
• 署名单位: American University; University of California System; University of California Berkeley
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/003465304774201860
• 发表日期: 2004-02
• 页码: 433-438
• 关键词: time-series

摘要

We use a nonlinear, nonparametric method to forecast unemployment rates. This method is an extension of the nearest-neighbor method but uses a higher-dimensional simplex approach. We compare these forecasts with several linear and nonlinear parametric methods based on the work of Montgomery et al. (1998) and Carruth et al. (1998). Our main result is that, due to the nonlinearity in the data-generating process, the nonparametric method outperforms many other well-known models, even when these models use more information. This result holds for forecasts based on quarterly and on monthly data.