Forecasting Macroeconomic Labour Market Flows: What Can We Learn from Micro‐level Analysis?
利用德国失业保险个体数据，比较了计量、精算和统计方法在预测失业救济退出人数上的表现，发现基于个体数据的统计持续时间分析在六个月内的预测精度优于传统时间序列方法。
Abstract Forecasting labour market flows is important for budgeting and decision‐making in government departments and public administration. Macroeconomic forecasts are normally obtained from time series data. In this article, we follow another approach that uses individual‐level statistical analysis to predict the number of exits out of unemployment insurance claims. We present a comparative study of econometric, actuarial and statistical methodologies that base on different data structures. The results with records of the German unemployment insurance suggest that prediction based on individual‐level statistical duration analysis constitutes an interesting alternative to aggregate data‐based forecasting. In particular, forecasts of up to six months ahead are surprisingly precise and are found to be more precise than considered time series forecasts.