Collaborative Research: Monetary Policy in a Data-Rich Environment

合作研究：数据丰富环境中的货币政策

• 批准号: 0001708
• 负责人: Ben Bernanke
• 金额: $ 10.75万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-07-01 至 2002-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0001708&HistoricalAwards=false
• 关键词: Collaborative; Research; Monetary; Policy; Data

The collaborative and individual empirical research planned in this project will analyze the determinants and effects of monetary policy, taking explicitly into account the fact that policy is made in a data-rich environment in which monetary policymakers can exploit information contained in hundreds or even thousands of macroeconomic data series. Our study contrasts with most previous work, which typically assumes that the Federal Reserve's information set is spanned by ten variables or less. We will also take into account that monetary policy is made in real time; that is, instead of assuming (contrary to fact) that the Fed has final revisions of data at hand, we use only data that were actually available to the Fed at each date. To do this we use a real-time data set constructed by others to which we add non-revised data from various Federal Reserve documents and other federal agencies. Our basic task is to estimate policy reaction functions (PRFs) for the Federal Reserve, which relate the Fed's instrument to measures of the state of the economy. As far as possible, we want to allow the state of the economy to depend on the full range of macroeconomic data available at the time. To make estimation feasible we need a dimension-reduction scheme. We will apply a dynamic factor model approach, to be estimated using methods devised by others in a forecasting context. Besides allowing us to work with data sets in which the number of series exceeds the number of observations, this method permits us to deal systematically with data irregularities, including data of different frequencies and different spans. As already indicated, we are also able to incorporate the fact that at each date the Fed may be looking at a different vintage (revision) of the same underlying data series. We will allow for time variation in the parameters of the PRF, using a method that makes estimation of such models computationally easy in this context. Finally, the Fed's PRF can be estimated jointly with equations describing the rest of the economy, or in an unrestricted manner that leaves the rest of the economy unspecified.We are interested in addressing several questions with the empirical analyses. On the positive side we want to see if information-intensive PRF's provide a better description of the historical conduct of monetary policy than other alternatives. We also want to determine what types of information the Fed has historically responded to and to see if the information that appears to affect Fed decisions can be explained in terms of its predictive content for inflation and real activity. If that latter appear not to matter, then we will explore what can account for the Fed's behavior. If we are able to obtain sharper estimates of both the systematic and non-systematic components of monetary policy, we hope to use these results to refine recent work on the effects of each of these components on the economy. From a normative point of view, we would like to address the question of just how monetary policy should be conducted in a data-rich environment. This is a more complex question than the problem of forecasting with many variables, since the Fed's decision problem is dynamic and its loss function may have very different properties from a standard econometric loss function. We believe that addressing such questions will be of practical value, as well as providing a more realistic description of what the Fed actually does.

本项目计划的合作和个人实证研究将分析货币政策的决定因素和影响，明确考虑到政策是在数据丰富的环境中制定的这一事实，货币政策制定者可以利用包含在数百甚至数千个宏观经济数据系列中的信息。我们的研究与之前的大多数工作形成对比，这些工作通常假设美联储的信息集由十个或更少的变量组成。我们也会考虑到货币政策是实时制定的；也就是说，我们没有假设（与事实相反）美联储手头有最终的数据修订，而是只使用美联储在每个日期实际可用的数据。为了做到这一点，我们使用了一个由其他人构建的实时数据集，我们添加了来自各种美联储文件和其他联邦机构的未经修改的数据。我们的基本任务是估计美联储的政策反应函数（prf），它将美联储的工具与经济状况的衡量指标联系起来。我们希望尽可能让经济状况取决于当时所有可用的宏观经济数据。为了使估计可行，我们需要一个降维方案。我们将采用动态因素模型方法，在预测环境中使用其他人设计的方法进行估计。除了允许我们处理序列数超过观测数的数据集外，这种方法还允许我们系统地处理数据不规则性，包括不同频率和不同跨度的数据。如前所述，我们还可以考虑这样一个事实，即在每个日期，美联储可能会查看同一基础数据系列的不同年份（修订）。我们将允许PRF参数的时间变化，使用一种方法，使这种模型的估计在这种情况下易于计算。最后，美联储的PRF可以与描述经济其余部分的方程联合估计，或者以一种不受限制的方式，不指定经济其余部分。我们有兴趣用实证分析来解决几个问题。从积极的方面来看，我们希望看到信息密集的PRF是否比其他替代方法更好地描述了货币政策的历史行为。我们还想确定美联储历史上对什么类型的信息做出反应，看看这些似乎影响美联储决策的信息是否可以用其对通胀和实际活动的预测内容来解释。如果后者似乎无关紧要，那么我们将探讨什么可以解释美联储的行为。如果我们能够对货币政策的系统性和非系统性组成部分获得更准确的估计，我们希望利用这些结果来完善最近关于这些组成部分对经济的影响的工作。从规范的角度来看，我们想要解决的问题是，在一个数据丰富的环境中，货币政策应该如何实施。这是一个比多变量预测问题更复杂的问题，因为美联储的决策问题是动态的，其损失函数可能与标准计量损失函数具有非常不同的性质。我们认为，解决这些问题将具有实用价值，并对美联储的实际工作提供更现实的描述。