A Flexible Finite-Horizon Alternative to Long-Run Restrictions with an Application to Technology Shocks
提出一种最大化技术冲击对劳动生产率预测误差方差贡献的识别方法，在有限期内优于传统长期约束，应用于美国数据发现工时对正向技术冲击呈负响应。
Recent studies using long-run restrictions question the validity of the technology-driven real business cycle hypothesis. We propose an alternative identification that maximizes the contribution of technology shocks to the forecast-error variance of labor productivity at a long but finite horizon. In small-sample Monte Carlo experiments, our identification outperforms standard long-run restrictions by significantly reducing the bias in the short-run impulse responses and raising their estimation precision. Unlike its long-run restriction counterpart, when our Max Share identification technique is applied to U.S. data, it delivers the robust result that hours worked responds negatively to positive technology shocks. © 2014 The President and Fellows of Harvard College and the Massachusetts Institute of Technology