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Robustifying Learnability

机译：增强学习能力

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In recent years, the learnability of rational expectations equilibria (REE) and determinacy of economic structures have rightfully joined the usual performance criteria among the sought-after goals of policy design. Some contributions to the literature, including Bullard and Mitra [2002. Learning about monetary policy rules. Journal of Monetary Economics 49 (6), 1105-1139] and Evans and Honkapohja [2006. Monetary Policy, Expectations, and Commitment, Scandinavian Journal of Economics 108,15-38], have made significant headway in establishing certain features of monetary policy rules that facilitate learning. However a treatment of policy design for learnability in worlds where agents have potentially misspecified their learning models has yet to surface. This paper provides such a treatment. We begin with the notion that because the profession has yet to settle on a consensus model of the economy, it is unreasonable to expect private agents to have collective rational expectations. We assume that agents have only an approximate understanding of the workings of the economy and that their learning the reduced forms of the economy is subject to potentially destabilizing perturbations. The issue is then whether a central bank can design policy to account for perturbations and still assure the learnability of the model. We provide two examples one of which-the canonical New Keynesian business cycle model-serves as a test case. For different parameterizations of a given policy rule, we use structured singular value analysis (from robust control theory) to find the largest ranges of misspecifications that can be tolerated in a learning model without compromising convergence to an REE. In addition, we study the cost, in terms of performance in the steady state of a central bank that acts to robustify learnability on the transition path to REE.
机译：近年来，理性预期均衡（REE）的易学性和经济结构的确定性已正确地加入了通常的绩效标准，成为政策设计的目标。对文学的一些贡献，包括Bullard和Mitra [2002年。了解货币政策规则。货币经济学杂志49（6），1105-1139]和Evans和Honkapohja [2006年。货币政策，期望和承诺，《斯堪的纳维亚经济学杂志》 108,15-38]在建立有利于学习的货币政策规则的某些特征方面取得了重大进展。但是，在代理商可能错误地指定其学习模型的世界中，针对可学习性的策略设计的处理尚未浮出水面。本文提供了这种处理方法。我们从一个概念开始，因为由于该行业尚未建立在经济的共识模型上，所以期望私人代理人具有集体理性期望是不合理的。我们假设代理人仅对经济运作有一个大概的了解，并且他们学习经济的简化形式可能会扰乱稳定。然后，问题在于中央银行是否可以设计政策来解决干扰问题，并仍然确保模型的可学习性。我们提供了两个示例，其中一个示例（标准的新凯恩斯主义商业周期模型）用作测试用例。对于给定策略规则的不同参数化，我们使用结构化奇异值分析（来自鲁棒控制理论）来查找学习模型中可以容忍的最大范围的错误指定，而不会影响到REE的收敛。另外，我们根据中央银行在稳态下的性能来研究成本，该成本可增强向REE过渡路径上的可学习性。

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