Concept‐Based Bayesian Model Averaging and Growth Empirics
提出分层加权最小二乘法，处理回归方程中概念和测量两层不确定性，用于增长回归中不同增长决定因素的效应估计，结果直观且稳健。
Abstract In specifying a regression equation, we need to specify which regressors to include, but also how these regressors are measured. This gives rise to two levels of uncertainty: concepts (level 1) and measurements within each concept (level 2). In this paper we propose a hierarchical weighted least squares (HWALS) method to address these uncertainties. We examine the effects of different growth determinants taking explicit account of the measurement problem in the growth regressions. We find that estimates produced by HWALS provide intuitive and robust explanations. We also consider approximation techniques which are useful when the number of variables is large or when computing time is limited.