Learning and Shifts in Long-Run Productivity Growth
Learning and Shifts in Long-Run Productivity Growth
复制标题
长期生产力增长的学习和转变
DOI：
10.2139/ssrn.530745
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
John C. Williams
中科院分区：
文献类型：
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作者：
Rochelle M. Edge;Thomas Laubach;John C. Williams
很难实时区分生产率长期增长率的变化与短暂的波动。我们分析了20世纪70年代和90年代长期生产率增长预测的演变，并在动态一般均衡模型中检验了学习对长期生产率增长率变化的反应的后果。我们发现，基于估计卡尔曼滤波模型的实时数据更新规则非常好地描述了经济学家的长期生产率增长预测。然后，我们表明，学习对趋势生产率增长变化的影响具有深远的影响，并可以提高模型产生类似历史经验的反应的能力。如果立即认识到，长期增长率的提高会导致就业和投资的急剧下降，而随着学习，长期生产率增长率的上升会引发就业和投资的持续繁荣。
Shifts in the long-run rate of productivity growth are difficult, in real time, to distinguish from transitory fluctuations. We analyze the evolution of forecasts of long-run productivity growth during the 1970s and 1990s and examine in a dynamic general equilibrium model the consequences of learning on the responses to shifts in the long-run productivity growth rate. We find that an updating rule based on an estimated Kalman filter model using real-time data describes economists' long-run productivity growth forecasts extremely well. We then show that learning has profound implications for the effects of shifts in trend productivity growth and can improve the model's ability to generate responses that resemble historical experience. If immediately recognized, an increase in the long-run growth rate produces a sharp decline in employment and investment, while with learning, a rise in the long-run rate of productivity growth sets off a sustained boom in employment and investment.