New Methods for the Bayesian Estimation of DSGE Models

DSGE 模型贝叶斯估计的新方法

• 批准号: 0719405
• 负责人: Jesus Fernandez-Villaverde
• 金额: $ 39.73万
• 依托单位: National Bureau of Economic Research Inc
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0719405&HistoricalAwards=false
• 关键词: New; Methods; Bayesian; Estimation; DSGE

Abstract 0719405: New Methods for the Bayesian Estimation of DSGE ModelsJesus Fernandez-VillaverdeUniversity of Pennsylvania/NBERThis project develops new tools for the estimation of Dynamic Stochastic General Equilibrium (DSGE) models using a Bayesian approach. DSGE models are a concise and simplified representation of the economy of a country like the United States. The models start by specifying the behavior of households, firms, and the government, with a special emphasis in the dynamic aspects of the decisions of families and firms. Then, the models carefully sum up all these decisions into aggregate variables and study how the economy as a whole reacts to different shocks and to changes in fiscal and monetary policies.DSGE models are a popular research tool to understand the U.S. economy. Moreover, an increasing number of policy-making institutions, both in the United States (the Federal Reserve Board and several of the regional Federal Reserve Banks, the International Monetary Fund) and abroad (the European Central Bank, the Bank of England, the Bundesbank, the Central Banks of Austria, Canada, Italy, Spain, and Sweden, to name a few) employ DSGE models to help in the formulation of better economic policies. Finally, economists are accumulating evidence of the good forecasting performance of DSGE models, even when compared with judgmental predictions from staff economists at the Federal Reserve System.All these three type of exercises (research to understand the U.S. economy, model specification to help formulate economic policy, and forecasting) require the estimation of the model, i.e., to use real data to make the model "fit" the real world as well as possible. Bayesian methods are especially suitable for this task since they efficiently summarize the sample information and mix it with the prior information in a flexible way. Moreover, recent advances in computation make the implementation of the Bayesian approach straightforward, robust, and direct.However, this estimation of DSGE models is a challenging task. DSGE models are complex structures. In addition, their statistical properties are not fully understood and economists have been forced to make simplifying assumptions that limit the applicability of the methodology.This project develops new tools to estimate DSGE models. The unifying view of the research agenda is simple: making the estimation of DSGE models more flexible. Economists want to capture richer dynamics and relax some of the tight assumptions that they currently impose to estimate DSGE models. The project has three parts. First, it finds how to perform Bayesian estimation of Markov-switching DSGE models. Second, it shows how to undertake semiparametric Bayesian estimation of DSGE models. Third, it studies the estimation of dynamic games in macroeconomics with a semi-parametric Bayesian approach.The newer and better tools that this proposal outlines are designed explicitly for the purpose of helping the Federal Reserve Board and other policy-making institutions develop more flexible models that will contribute to the implementation of an effective monetary policy in the United States. Finally, many of the tools outlined in the proposal have potential applications in other fields of economics (such as international economics, industrial organization, or labor economics), and other social sciences where researchers want to estimate dynamic models using flexible, yet powerful tools.

摘要0719405：贝叶斯估计DSGE ModelsJesus Fernandez-Villaverde宾夕法尼亚大学/NBERThis项目开发了新的工具，估计动态随机一般均衡（DSGE）模型使用贝叶斯方法。DSGE模型是对美国等国家经济的简明和简化的表示。该模型从具体说明家庭、企业和政府的行为开始，特别强调家庭和企业决策的动态方面。然后，将所有这些决策汇总为汇总变量，研究经济整体对各种冲击和财政、货币政策变化的反应。DSGE模型是了解美国经济的常用研究工具。此外，越来越多的决策机构，无论是在美国，许多机构（联邦储备委员会和几个地区性的联邦储备银行、国际货币基金组织）和国外机构（欧洲中央银行、英格兰银行、德国央行、奥地利、加拿大、意大利、西班牙和瑞典等国的中央银行）都采用DSGE模型来帮助制定更好的经济政策。最后，经济学家们正在积累证据，证明DSGE模型具有良好的预测性能，即使与联邦储备系统的经济学家的判断性预测相比也是如此。所有这三种类型的练习（了解美国经济的研究，帮助制定经济政策的模型说明和预测）都需要对模型进行估计，即，使用真实的数据使模型尽可能地“适合”真实的世界。贝叶斯方法特别适合于这一任务，因为它们有效地总结了样本信息，并以灵活的方式将其与先验信息混合。此外，最近的进展，在计算贝叶斯方法的实施简单，鲁棒性，和直接。然而，这种估计DSGE模型是一个具有挑战性的任务。DSGE模型是复杂的结构。此外，由于对DSGE模型的统计特性还不完全了解，经济学家不得不进行简化假设，从而限制了方法的适用性。本项目开发了估计DSGE模型的新工具。研究议程的统一观点很简单：使DSGE模型的估计更加灵活。经济学家希望捕捉到更丰富的动态，并放松他们目前用来估计DSGE模型的一些严格假设。该项目有三个部分。首先，它发现如何执行马尔可夫切换DSGE模型的贝叶斯估计。其次，介绍了如何对DSGE模型进行半参数贝叶斯估计。第三，用半参数贝叶斯方法研究宏观经济学中的动态博弈的估计。本提案所提出的更新、更好的工具，是为了帮助联邦储备委员会等决策机构开发更灵活的模型，为美国有效实施货币政策做出贡献而设计的。最后，提案中概述的许多工具在其他经济学领域（如国际经济学、产业组织或劳动经济学）和其他社会科学中具有潜在的应用，研究人员希望使用灵活而强大的工具来估计动态模型。