New Methods for the Bayesian Estimation of DSGE Models

摘要：DSGE模型贝叶斯估计的新方法jesus fernandez - villaverde宾夕法尼亚大学/ nberi该项目开发了使用贝叶斯方法估计动态随机一般均衡（DSGE）模型的新工具。DSGE模型是对像美国这样的国家的经济的一个简明和简化的表示。这些模型首先详细说明家庭、企业和政府的行为，特别强调家庭和企业决策的动态方面。然后，这些模型仔细地将所有这些决定总结为总变量，并研究经济作为一个整体如何应对不同的冲击以及财政和货币政策的变化。DSGE模型是了解美国经济的流行研究工具。此外，越来越多的决策机构，无论是在美国（联邦储备委员会和几个地区联邦储备银行、国际货币基金组织）还是在国外（欧洲中央银行、英格兰银行、德国央行、奥地利、加拿大、意大利、西班牙和瑞典的中央银行，仅举几例），都采用DSGE模型来帮助制定更好的经济政策。最后，经济学家正在积累证据，证明DSGE模型具有良好的预测性能，即使与美联储（fed）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（研究以了解美国经济，模型规范以帮助制定经济政策，预测）都需要对模型进行估计，即使用真实数据使模型尽可能地“适合”现实世界。贝叶斯方法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在一起，特别适合于这一任务。此外，计算方面的最新进展使贝叶斯方法的实现变得简单、健壮和直接。然而，对DSGE模型的估计是一项具有挑战性的任务。DSGE模型是复杂的结构。此外，它们的统计特性并没有被完全理解，经济学家被迫做出简化的假设，这限制了方法的适用性。该项目开发了新的工具来估计DSGE模型。研究议程的统一观点很简单：使DSGE模型的估计更加灵活。经济学家希望捕捉更丰富的动态，并放宽他们目前在估计DSGE模型时强加的一些严格假设。这个项目有三个部分。首先，研究了如何对马尔可夫切换DSGE模型进行贝叶斯估计。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了宏观经济学中动态博弈的估计。本提案概述的更新和更好的工具的设计目的是明确地帮助联邦储备委员会和其他决策机构开发更灵活的模型，这些模型将有助于在美国实施有效的货币政策。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）和其他社会科学领域都有潜在的应用，在这些领域，研究人员希望使用灵活而强大的工具来估计动态模型。

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.