Learning-by-Doing as a Propagation Mechanism
提出，通过过去工作经验积累技能（干中学）能成为动态随机一般均衡模型中的重要传导机制，因为当前劳动供给影响未来生产率。使用贝叶斯方法结合微观面板数据和宏观时间序列，模型评估表明引入干中学机制改善了模型拟合总产出和工时动态的能力。
This paper suggests that skill accumulation through past work experience, or “learning-by-doing” (LBD), can provide an important propagation mechanism in a dynamic stochastic general-equilibrium model, as the current labor supply affects future productivity. Our econometric analysis uses a Bayesian approach to combine micro-level panel data with aggregate time series. Formal model evaluation shows that the introduction of the LBD mechanism improves the model's ability to fit the dynamics of aggregate output and hours.