Collaborative Research: Piecewise Linear Approximations for DSGE Models With Occasionally-Binding Constraints: Solution, Estimation, Model Evaluation, and Applications

中央银行监管机构使用数学模型来了解经济活动的波动和银行政策的影响。 目前用于分析政策变化对大规模经济变量（如总产出、通货膨胀和利率）之间关系的一般影响的方法要么无法捕捉某些极端事件（如利率降至零）的影响，要么实施起来太慢且成本太高。 该研究项目将开发新的，更快，更便宜，更好的方法来分析政策变化对经济中大规模变量的影响。 因此，本研究项目的结果将有助于经济科学，更重要的是，可以更好地了解政策变化或外部影响如何在整个经济体中传播。 这将使经济学家能够为政策制定者提供更明智的建议，从而促进经济增长，提高美国人的生活水平。 该项目开发的工具也可以在世界任何地方使用，从而确立了美国在经济分析工具开发方面的全球领先地位。在大衰退之前，美联储等监管机构使用的宏观变量之间的线性关系模型能够捕捉到汇总时间序列的最重要特征，并做出准确的预测。然而，在大衰退期间和之后，非线性所产生的约束条件，如名义利率的有效下限约束，已成为常态。 本研究项目将开发新技术，以构建宏观经济模型的非线性解决方案，其中约束约束条件发挥了重要作用。 拟议研究的目标是开发和应用一种分段线性求解方法，该方法在精度和计算速度之间进行权衡，并可扩展到中央银行使用的大型模型。该项目还将开发一类灵活的模型，可用于评估施加强理论限制的模型是否得到很好的指定。 该项目的成果将为研究人员提供新的分析工具，从而提高政策咨询和监管的质量。 该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Central banks regulators use mathematical models to understand fluctuations in economic activity and the effects of the bank's policies. Current methods used to analyze the general effects of policy changes on relationships among large-scale economic variables, such as total output, inflation, and interest rate are either not able to capture the effects of some extreme events, such as decreasing interest to zero, or are too slow and costly to implement. This research project will develop new, faster, cheaper, and better methods to analyze the effects of policy changes on large-scale variables in the economy. The results of this research project will therefore contribute to economic science, and more important, allow for a better understanding of how policy changes or external influences are transmitted throughout the larger economy. This will allow economist to provide more informed advice to policymakers and in so doing, enhance economic growth and improve the living standards of Americans. The tools developed in this project can also be applied anywhere in the world, thus establishing the U.S. as the global leader in the development of economic analytical tools.Prior to the Great Recession, models of linear relationship among macro variables used by the Fed and other regulatory agencies were able to capture the most important features of aggregate time series and generate accurate predictions. However, during and after the Great Recession, nonlinearities generated by occasionally-binding constraints such as an effective lower bound constraint on nominal interest rates, have become the norm. This research project will develop new techniques to construct nonlinear solutions to macroeconomic models in which occasionally-binding constraints play an important role. The goal of the proposed research is to develop and apply a piecewise-linear solution method that trades off a little of accuracy against computational speed and is scalable to large models used by central banks. The project will also develop a class of flexible models that can be used to assess whether models that impose strong theoretical restrictions are well specified. The results of this project will provide researchers with new analytical tools that will improve the quality of policy advice as well as regulation. This could increase economic growth in the U.S. and improve the lives of citizens.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.