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首页> 外文期刊>Journal of Economic Dynamics and Control >Learning in an estimated medium-scale DSGE model
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Learning in an estimated medium-scale DSGE model

机译：在估计的中等规模DSGE模型中学习

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摘要

We evaluate the empirical relevance of learning by private agents in an estimated medium-scale DSGE model. We replace the standard rational expectations assumption in the Smets and Wouters (2007) model by a constant-gain learning mechanism. If agents know the correct structure of the model and only learn about the parameters, both expectation mechanisms produce very similar results, and only the transition dynamics that are generated by specific initial beliefs seem to improve the fit. If, instead, agents use only a reduced information set in forming the perceived law of motion, the implied model dynamics change and, depending on the specification of the initial beliefs, the marginal likelihood of the model can improve significantly. These best-fitting models add additional persistence to the dynamics and this reduces the gap between the IRFs of the DSGE model and the more data-driven DSGE-VAR model. However, the learning dynamics do not systematically alter the estimated structural parameters related to the nominal and real frictions in the DSGE model.
机译：我们在估计的中等规模的DSGE模型中评估私人代理商学习的经验相关性。我们用恒定收益学习机制代替了Smets and Wouters（2007）模型中的标准理性预期假设。如果主体知道模型的正确结构并且仅了解参数，则两种期望机制都会产生非常相似的结果，并且只有由特定初始信念生成的过渡动力学才能改善拟合度。相反，如果代理商仅在形成感知的运动定律时使用简化的信息集，则隐含的模型动力学会发生变化，并且根据初始信念的规范，模型的边际可能性会大大提高。这些最适合的模型为动态特性增加了额外的持久性，从而缩小了DSGE模型的IRF与更多数据驱动的DSGE-VAR模型之间的差距。但是，学习动力学不会系统地更改与DSGE模型中的名义和实际摩擦有关的估计结构参数。

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