DSGE Models and Optimal Monetary Policy in a Data-Rich Environment

最近的宏观经济研究致力于开发和估计内部一致的、基于第一原理的动态随机均衡(DSGE)模型。最近的一些微观建立的离散一般均衡模型，涉及许多摩擦和各种类型的冲击，似乎在重要维度上复制了数据，并具有可观的样本外预测性能。在一定程度上受到这些有希望的结果的推动，这些模型现在越来越多地被视为有用的预测工具，以及对政策制定有价值的投入。然而，这些经验模型的一个潜在重要局限性是，它们只利用了少数几个宏观经济指标。这与中央银行和市场参与者监测数百个经济指标的事实不符，也与强调大套宏观经济指标重要性的最新预测模型不一致。该项目的第一步为有效利用大量可用数据开发了一个估计动态随机一般均衡模型的一般经验框架。不是假设理论概念(例如，通货膨胀和产出)可以通过单一的数据序列进行适当的衡量，而是将这些概念视为通过许多噪声指标观察到的不完美。还可以利用与模型概念有未知关系的指标提供的信息。该估计基于一种新的MCMC算法，它有效地处理了估计问题的高维问题，并继承了经典最大似然估计的性质。提出的经验框架有几个优点：1)它有助于更好地从测量误差中识别结构性冲击；2)它潜在地使估计更有效，从而可以改善模型的预测性能；3)它产生具有明确经济解释的潜在变量，不同于动态因素模型在宏观经济中的应用；4)它对数据集中包括的所有序列进行预测；5)它提供了一种自然的方式来记录结果对先验的敏感性。第二步将该经验框架应用于最先进的DSGE模型，该模型是Smets和Wouters(2004)模型的变体，已经显示出有希望的经验成功。初步结果表明，增加更多的信息会影响有关经济周期的重要结论，并显著提高模型的预测性能。第三步，推导出数据丰富的稳健最优货币政策目标准则。现有的派生工具对如何以最佳方式利用大数据集中的信息只字不提，这可能是一个关键的操作考虑因素。该项目的这一部分在数据丰富的经验框架内，将得出稳健最优目标标准的现有程序与不完全信息下最优货币政策的现有结果结合在一起。广泛影响：本建议的目标是有助于开发一个能够产生有用的、实时的政策处方的操作模型。拟议中的方法可以通过结构模型的镜头，帮助央行系统地实时处理大量信息，该模型保持了各种经济发展、预测和政策处方之间的可解释联系。它还可能对私营部门有用，为一系列经济指标提供最佳政策立场的基准和隐含的预测。这个项目的一个具体成果是开发一个基础设施，使私人投资机构能够连续地估计、预测和实时计算最佳政策设定，并将在网站上公布(连同估计所依据的数据和各种计算机例程)。最后，拟议框架的操作便利性使其作为一种教学工具特别有吸引力。调查人员计划根据这一提议的结果，扩大现有的一个MBA案例。

Recent macroeconomic research has devoted considerable efforts to the development and estimation of dynamic stochastic equilibrium (DSGE) models that are internally consistent, and based on first principles. Some recent micro-founded DSGE models, which involve numerous frictions and various types of shocks, appear to replicate the data in important dimensions and have appreciable out-of-sample forecasting performance. In part motivated by these promising results, these models are now increasingly perceived as useful forecasting devices and as valuable inputs to policy making. However, one potentially important limitation of these empirical models is that they exploit only a handful of macroeconomic indicators. This is at odds with the fact that central banks and market participants monitor hundreds of economic indicators and is inconsistent with state-of-the-art forecasting models that emphasize the importance of large sets of macroeconomic indicators.The first step of this project develops a general empirical framework for the estimation of DSGE models which makes efficient use of the large amount of available data. Instead of assuming that theoretical concepts (e.g., inflation and output) are properly measured by a single data series, these are treated as imperfectly observed through many noisy indicators. Information from indicators that have an unknown relationship with the model's concepts can also be exploited. The estimation is based on a new variant of an MCMC algorithm, which deals effectively with the high dimensionality of the estimation problem, and can inherit the properties of classical maximum likelihood estimation. The proposed empirical framework has several advantages: 1) it helps to better identify structural shocks from measurement errors; 2) it potentially makes the estimation more efficient which could improve the model's forecasting performance; 3) it yields latent variables that have a clear economic interpretation, unlike in macroeconomic applications of dynamic factor models; 4)it has predictions for all series included in the data set; 5) it provides a natural way to document the sensitivity of the results to the priors.The second step applies this empirical framework to a state-of-the-art DSGE model, a variant of the Smets and Wouters (2004) model, which has shown promising empirical successes. Preliminary results show that adding more information affects important conclusions about business cycles and improves significantly the model's forecasting performance.The third step derives a data-rich robustly optimal target criterion for monetary policy. Existing derivations are silent about how to optimally exploit information from large data sets, which might be a crucial operational consideration. This part of the project combines, within the data-rich empirical framework, existing procedures for the derivation of a robustly optimal target criterion, with existing results for optimal monetary policy under imperfect information.Broader Impacts: The objective of this proposal is to contribute to the development of an operational model that can produce useful, real-time, policy prescriptions. The proposed approach could help central banks systematically process a large amount of information in real-time, through the lens of a structural model that maintains an interpretable link between various economic developments, forecasts and policy prescriptions. It could also be useful to the private sector, by providing a benchmark of the optimal policy stance and implied forecasts for a wide range of economic indicators. A concrete output of this project is to develop an infrastructure allowing the PIs to continuously estimate, forecast, and calculate the optimal policy setting in real time, which will be published on a website (together with the data and various computer routines underlying the estimation). Finally, the operational convenience of the proposed framework makes it particularly appealing as a teaching tool. The investigators plan to expand an existing MBA case on the basis of the results from this proposal.