A novel multivariate composite estimator for the labour force survey
提出一种新型多元复合估计量，同时建模所有劳动力市场类别，利用类别间相关性提高估计精度，并允许估计随时间变化的波次偏差，在挪威劳动力调查数据上显示比直接估计量更优的变动估计精度。
Abstract This paper introduces a novel multivariate composite estimator for the Labour Force Survey (LFS). The estimator improves upon traditional methods by simultaneously modelling all labour market categories, such as employment, unemployment, and nonparticipation. This multivariate approach avoids the asymmetrical treatment of a residual category and leverages the correlation structure between the different labour market categories. The paper also presents a framework for explicitly incorporating and estimating wave-specific biases, which can vary over time. We derive analytical formulas for the estimator’s variance and demonstrate its properties using data from the Norwegian LFS. The empirical results show that the estimator provides substantial precision gains for estimates of change over time compared to the direct estimator. By not assuming a smooth trend for the underlying population values, the proposed method also offers a robust alternative to state–space models, particularly during periods of high economic volatility.