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Learning and optimal monetary policy

机译:学习与最佳货币政策

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To conduct policy efficiently, central banks must use available data to infer, or learn, the relevant structural relationships in the economy. However, because a central bank's policy affects economic outcomes, the chosen policy may help or hinder its efforts to learn. This paper examines whether real-time learning allows a central bank to learn the economy's underlying structure and studies the impact that learning has on the performance of optimal policies under a variety of learning environments. Our main results are as follows. First, when monetary policy is formulated as an optimal discretionary targeting rule, we find that the rational expectations equilibrium and the optimal policy are real-time learnable. This result is robust to a range of assumptions concerning private-sector learning behavior. Second, when policy is set with discretion, learning can lead to outcomes that are better than if the model parameters are known. Finally, if the private sector is learning, then unannounced changes to the policy regime, particularly changes to the inflation target, can raise policy loss considerably.
机译:为了有效地执行政策,中央银行必须使用可用数据来推断或学习经济中的相关结构关系。但是,由于中央银行的政策会影响经济成果,因此选择的政策可能有助于或阻碍其学习努力。本文研究了实时学习是否可以使中央银行学习经济的基础结构,并研究在各种学习环境下学习对最佳政策绩效的影响。我们的主要结果如下。首先,当将货币政策制定为最优自由裁量目标规则时,我们发现理性预期均衡和最优政策是实时可学习的。该结果对于涉及私营部门学习行为的一系列假设是可靠的。其次,当政策是自由决定的时,学习可以产生比已知模型参数更好的结果。最后,如果私营部门正在学习,那么对政策制度的未经宣布的变化,特别是对通货膨胀目标的变化,可能会大大增加政策损失。

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