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

协作研究：具有偶尔约束约束的 DSGE 模型的分段线性逼近：解决方案、估计、模型评估和应用

• 批准号: 1851093
• 负责人: Sadik Boragan Aruoba
• 金额: $ 21.5万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1851093&HistoricalAwards=false
• 关键词: Collaborative; Research; Piecewise; Linear; Approximations

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 police makers 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.

中央银行的监管机构使用数学模型来了解经济活动的波动以及银行政策的影响。 当前用于分析政策变化对大规模经济变量之间关系的一般影响的方法，例如总产出，通货膨胀和利率，无法捕获某些极端事件的影响，例如减少零利息的效果，或者太慢且实施太昂贵。 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大型变量的影响。 因此，该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较大的经济中传播。 这将使经济学家能够为警察制造商提供更多知识的建议，并因此增强经济增长并提高美国人的生活水平。 该项目中开发的工具也可以在世界任何地方应用，从而使美国成为经济分析工具开发的全球领导者。但是，在大衰退期间和之后，偶尔结合限制产生的非线性（例如对标称利率的有效下限约束）已成为常态。 该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，其中偶尔结合的约束起着重要作用。 拟议的研究的目的是开发和应用分段线性解决方案方法，该方法可以针对计算速度进行一些准确性，并且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，这些模型可用于评估施加强大理论限制的模型是否得到很好的指定。 该项目的结果将为研究人员提供新的分析工具，以提高政策建议和法规的质量。 这可能会增加美国的经济增长并改善公民的生活。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来评估的。

项目成果

SVARs With Occasionally-Binding Constraints

• DOI: 10.1016/j.jeconom.2021.07.013
• 发表时间: 2021-11
• 期刊: SSRN Electronic Journal
• 作者: S. B. Aruoba;Marko Mlikota;F. Schorfheide;Sergio Villalvazo

Piecewise-linear approximations and filtering for DSGE models with occasionally-binding constraints

具有偶尔约束约束的 DSGE 模型的分段线性近似和过滤

• DOI: 10.1016/j.red.2020.12.003
• 发表时间: 2021
• 期刊: Review of Economic Dynamics
• 影响因子: 2
• 作者: Aruoba, S. Borağan;Cuba-Borda, Pablo;Higa-Flores, Kenji;Schorfheide, Frank;Villalvazo, Sergio
• 通讯作者: Villalvazo, Sergio