论文标题
预测非农场就业
Predicting Non Farm Employment
论文作者
论文摘要
美国非农业就业被认为是评估劳动力市场状况的关键指标之一。与期望的相当大的偏差可能会导致市场移动的影响。在本文中，预计在发布BLS就业报告之前，预计美国非农业薪资就业。此处的内容概述了从汇总的工资数据和培训机器学习模型中提取预测功能的过程，以进行准确的预测。 BLS的公开修订的就业报告被用作基准。训练有素的模型在2012年1月至2020年3月的样本周期内显示出极好的行为，R2为0.9985和99.99％的方向准确性。 关键字机器学习；经济指标；结；回归，总非农业工资
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. Publically available revised employment report by BLS is used as a benchmark. Trained models show excellent behaviour with R2 of 0.9985 and 99.99% directional accuracy on out of sample periods from January 2012 to March 2020. Keywords Machine Learning; Economic Indicators; Ensembling; Regression, Total Nonfarm Payroll
