Monetary Policy with Model Uncertainty: Distribution Forecast Targeting
Monetary Policy with Model Uncertainty: Distribution Forecast Targeting
复制标题
具有模型不确定性的货币政策：分配预测目标
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
Noah Williams
中科院分区：
文献类型：
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作者：
Lars E. O. Svensson;Noah Williams
我们研究了最优和其他货币政策的线性二次设置与一个相对一般形式的模型不确定性，所谓的马尔可夫跳跃线性二次系统扩展到包括前瞻性变量。我们的框架所涵盖的模型不确定性的形式包括：简单i.i.d.模型偏差;连续相关的模型偏差;可估计的状态转换模型;关于非常不同的模型的更复杂的结构不确定性，例如，向后和向前展望的模型;时变的中央银行关于模型不确定性状态的判断;等等。我们提供了一个算法，找到最佳的政策，以及解决方案的任意政策功能。这使我们能够计算和绘制一致的分布预测--扇形图--目标变量和工具。因此，我们的方法扩展了确定性等价和“平均预测目标”更一般的确定性不等价和“分布预测目标”。"
We examine optimal and other monetary policies in a linear-quadratic setup with a relatively general form of model uncertainty, so-called Markov jump-linear-quadratic systems extended to include forward-looking variables. The form of model uncertainty our framework encompasses includes: simple i.i.d. model deviations; serially correlated model deviations; estimable regime-switching models; more complex structural uncertainty about very different models, for instance, backward- and forward-looking models; time-varying central-bank judgment about the state of model uncertainty; and so forth. We provide an algorithm for finding the optimal policy as well as solutions for arbitrary policy functions. This allows us to compute and plot consistent distribution forecasts---fan charts---of target variables and instruments. Our methods hence extend certainty equivalence and "mean forecast targeting" to more general certainty non-equivalence and "distribution forecast targeting."