预测非农就业
引用次数: 0
摘要
美国非农就业被认为是评估劳动力市场状况的关键指标之一。与预期有相当大的偏差会对市场产生影响。本文在美国劳工统计局发布就业报告之前对美国非农就业总人数进行了预测。本文的内容概述了从汇总工资数据中提取预测特征和训练机器学习模型以做出准确预测的过程。美国劳工统计局公布的经修订的就业报告被用作基准。经过训练的模型在2012年1月至2020年3月的样本外周期中表现出出色的行为，r2为0.9985，方向精度为99.99%。应用计算方法学本文章由计算机程序翻译，如有差异，请以英文原文为准。
Predicting Nonfarm Employment
U.S. Nonfarm employment is considered one of the key indicators for assessing the state of the labor market. Considerable deviations from the expectations can cause market moving impacts.
In this paper, the total U.S. nonfarm payroll employment is predicted before the release of the BLS employment report. The content herein outlines the process for extracting predictive features from the aggregated payroll data and training machine learning models to make accurate predictions. Publicly available revised employment report by BLS is used as a benchmark. Trained models show excellent behaviour with R 2 of 0.9985 and 99.99% directional accuracy on out of sample periods from January 2012 to March 2020.
CCS Concepts
• Applied Computing methodologies➝Machine Learning.
In this paper, the total U.S. nonfarm payroll employment is predicted before the release of the BLS employment report. The content herein outlines the process for extracting predictive features from the aggregated payroll data and training machine learning models to make accurate predictions. Publicly available revised employment report by BLS is used as a benchmark. Trained models show excellent behaviour with R 2 of 0.9985 and 99.99% directional accuracy on out of sample periods from January 2012 to March 2020.
CCS Concepts
• Applied Computing methodologies➝Machine Learning.
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