Theory and Inference for Macroeconomic Policy

宏观经济政策的理论和推理NSF SES 0719055普林斯顿大学的克里斯托弗·A·西姆斯这项研究在四个领域取得了进展，这些领域与使用量化模型指导宏观经济政策有关。一个组成部分是研究具有大量参数的模型的统计推断的基础。中央银行和其他宏观经济政策机构使用的模型往往涉及数百个参数，这些参数的值必须从数据中确定，然而，现有的许多关于经济时间序列模型中的推断的文献侧重于仅在参数数量较少时适用的方法。一些统计学家认为，当有大量参数时，作为标准决策理论基础的贝叶斯推理方法表现不佳。虽然这一断言在一些特定模型的背景下遭到了反驳，但该项目的研究开发了对这些问题的更普遍的处理方法。现在，构建央行货币政策分析所需的规模的模型现在首次变得可行，这些模型在统计上是可靠的，并配备了对构成模型的方程的详细解释，从经济行为的角度来看。然而，仍然有几种相互竞争的方法，尤其是在对统计可靠性的相对权重与关于方程式的经济行为故事方面。本项目的第二部分提出了一种特定的方法来进行此类建模。在试图讲述行为故事的同时，同时保持统计可靠性，这一领域的许多现有工作最终都得到了行为模型，其中包括摩擦和“调整成本”，这些都是匹配数据所必需的，但作为行为故事，它们并不完全令人信服。该项目将行为模型视为数据本身的描述，而不是对可靠统计模型的行为的近似、概率预测的来源，特别是从长期来看。具体地说，该项目开发了使用完全解释的均衡模型来生成结构向量自回归(SVaR)参数的先验分布的方法。均衡模型是sVaR的受限版本，并且限制是概率放松的，特别是在我们认为行为模型不允许实际复杂的摩擦和惯性的高频下。这种方法可能不需要在行为模型中依赖看起来随意的调整成本，而不会牺牲统计可靠性。如果我们认识到经济主体只能以有限的速度处理信息，那么在给定信息约束的情况下，行为的许多惯性和随机方面都是最优的。该项目将之前的研究扩展到更现实的市场互动模型，之前的研究只针对简单的单行为者模型正式地发展了这一见解。在某种程度上，该项目的第三部分是对第二部分的补充，因为它寻求为行为模型的动力学提供更坚实的微观基础理论。该项目的第四部分涉及政府债务和赤字之间的关系以及通胀控制。只要市场相信，如果债务变大，赤字将被削减，如果债务变得非常小，赤字将会增加，那么通常的货币政策利率设定工具就可以控制价格水平，而不需要参考财政变量。但在许多国家，市场实际上没有这种信心，这导致了货币政策和财政政策之间复杂的相互作用。本项目对这些相互作用进行了实证研究。随着人口老龄化带来的财政压力增加，这一结果可能与美国的政策相关。

Theory and Inference for Macroeconomic PolicyNSF SES 0719055Christopher A. Sims, Princeton UniversityThis research advances knowledge in four areas related to the use of quantitative models to guide macroeconomic policy. One component is study of the foundations of statistical inference for models with large numbers of parameters. The models in use at central banks and other macroeconomic policy institutions often involve hundreds of parameters whose values have to be determined from the data, yet much of the existing literature on inference in models of economic time series focuses on methods that apply only when the number of parameters is small. Some statisticians have argued that when there are large numbers of parameters, the Bayesian inferential methods that underlie standard decision theory perform poorly. While this assertion has been rebutted in the context of some specific models, this project's research develops a more general treatment of the issues.It is now for the first time becoming feasible to construct models of the scale needed in monetary policy analysis at central banks that are both statistically reliable and equipped with a detailed interpretation, in terms of economic behavior, of the equations that constitute the model. There remain several competing approaches, however, varying especially in the relative weight put on statistical reliability versus economic behavioral stories about the equations. The second component of this project advances a particular approach to this type of modeling. In attempting to tell behavioral stories, while at the same time preserving statistical reliability, much of the existing work in this area has ended up with behavioral models that include frictions and "costs of adjustments" that are necessary to match the data, but not fully convincing as behavioral stories. This project treats the behavioral model not as itself a description of the data, but instead as a source of approximate, probabilistic predictions about the behavior of a reliable statistical model, particularly in the long run. Specifically, the project develops methods to use a fully interpreted equilibrium model to generate a prior distribution for the parameters of a structural vector auto-regression (SVAR). The equilibrium model is a restricted version of the SVAR, and the restrictions are relaxed probabilistically, especially at high frequencies where we think the behavioral model does not allow for realistically complex frictions and inertias. This approach may make it unnecessary to rely on arbitrary-seeming adjustment costs in the behavioral model, with no sacrifice in statistical reliability. If we recognize that economic agents can process information at only a finite rate, many of the inertial and random aspects of behavior emerge as optimal, given information constraints. This project extends previous research, which has developed this insight formally only for simple, single-actor models, to more realistic models of market interaction. In a way this third part of the project complements the second, as it seeks to provide a more firmly micro-founded theory for the dynamics of behavioral models.The fourth component of the project concerns the relation between government debt and deficits and control of inflation. So long as markets are confident that deficits will be cut if debt grows large and increased if it grows very small, the usual interest-rate-setting tools of monetary policy can control the price level without reference to fiscal variables. But in many countries markets realistically do not have this confidence, which results in complex interactions between monetary and fiscal policy. These interactions are studied empirically in this project. The results could be relevant to US policy as the fiscal pressures of from an aging population increase.