Collaborative Research: Perturbation Methods for Markov-Switching Models
Collaborative Research: Perturbation Methods for Markov-Switching Models
批准号:
1223271
负责人:
Jesus Fernandez-Villaverde
金额:
$19.98万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2012
资助国家:
美国
项目状态:
已结题
起止时间:
2012-09-01 至 2017-08-31
中文摘要
建议题目:协作性研究:马尔可夫切换模型的摄动方法建议编号:SES-1223198这个建议旨在开发新的工具,用于计算和估计宏观经济学中的动态模型,并将这些工具应用于相关的政策问题。动态模型已成为现代宏观经济学的标准工具。因为它们是用来分析经济如何随时间演变的,所以它们被用来研究增长和商业周期,设计货币和财政政策,或者调查金融和劳动力市场的总体方面,以及其他许多任务。然而,应用宏观经济研究人员的工具箱中仍然缺少许多工具。例如,经济学家没有很好地理解政策制度变化的影响--即经济政策相对于一个制度内的变化而有系统地实施的方式的变化--或者对未来政策的信念如何影响家庭和公司当前的行为。这项提案的目标是为这些任务提供一些必要的工具,并展示如何利用这些工具来解决公共政策设计和评估中的重要问题。因此,这项新研究的很大一部分可能会对宏观领域的其他经济学家产生正外部性,更广泛地说,对也使用动态模型的其他领域的研究人员来说。具体地说,本文的重点是如何使用摄动方法从第一原理出发,即从描述经济主体行为的非线性化最优条件集出发,而不是像文献所做的那样,从线性化条件集出发,使用摄动方法来求解马尔可夫切换理性预期(MSRE)模型。微扰法是自然科学和经济学中常用的一种方法,用来建立解析上难以解决的模型的近似解。MSRE模型考虑了不同可能的政策体制(例如,鹰派央行行长和鸽派央行行长)随着时间的推移而演变和转换(鹰派央行行长之后紧随其后的是鸽派央行行长,等等)。MSRE模型推导出了一组需要求解的代数方程,以便使用微扰求出政策函数的一阶泰勒展开。然后,展示了基于奇异值分解(SVD)算法的传统方法在常参数情况下是如何不适用于MSRE模型的。相反,该提案使用了格罗布纳基准法。然后,研究了如何检验确定性,并以一个具有名义刚性的经济周期模型为例进行了求解。最后,该方案指出了微扰方法如何也允许我们找到MSRE模型解的高阶近似。美国和国外的政策制定机构可以应用本提案中提出的方法。例如,联邦储备委员会和几家地区性联邦储备银行、国际货币基金组织、欧洲中央银行、英格兰银行以及奥地利、加拿大、德国、意大利、日本、西班牙和瑞典的中央银行(仅举几例)正在积极制定和评估动态宏观经济模型,用于政策分析和预测,这些模型可以受益于提案中提出的扩展类型。此外,经济学专业正在积累证据,证明这类模型的良好预测性能,即使与专业经济学家的判断性预测相比也是如此。该提案概述的更新和更好的工具是专门为帮助联邦储备委员会和其他决策机构开发更灵活的模型而设计的,这些模型将有助于在美国实施有效的公共政策。最后,新计算技术的开发在其他经济学领域(如国际经济学、产业组织或劳工经济学)以及其他社会科学领域具有潜在的应用前景,在这些领域,研究人员希望使用灵活但强大的工具来解决和估计动态模型。
英文摘要
AbstractProposal Title: Collaborative Research: Perturbation Methods for Markov-Switching ModelsProposal Number: SES - 1223198 This proposal aims to develop new tools for the computation and estimation of dynamic models in macroeconomics and for the application of those tools to relevant policy questions. Dynamic models have become a standard instrument in modern macroeconomics. Because they are built to analyze how the economy evolves over time, they are used to study growth and business cycles, to design monetary and fiscal policy, or to investigate the aggregate aspects of financial and labor markets, among many other tasks. However, many instruments are still missing in the toolbox of the applied macroeconomic researcher. For instance, economists do not have a good understanding of the effects of changes in policy regimes ?that is, variations in the way in which economic policy is systematically conducted in opposition to changes within one regime- or how the beliefs about future policies affect current behavior by households and firms.The goal of this proposal is to provide some of the required tools for these tasks and show how they can be used to address important questions in the design and evaluation of public policy. Consequently, much of this new research may have positive externalities for other economists within macro and, more generally, for researchers in other fields where dynamic models are also employed. In particular, this proposal focuses on how to use perturbation methods to solve Markov switching rational expectations (MSRE) models starting from first principles, that is, from the set of non-linearized optimality conditions that describe the behavior of the economic agents, rather than from the set of linearized ones, as the literature has previously done. Perturbation methods, commonly used in natural sciences and economics, built approximated solution to models that are analytically intractable. MSRE models allow for different possible policy regimes (for instance, a hawkish central banker and a dovish central banker) that evolve and switch over time (hawkish central bankers are followed, with some probability, by dovish central bankers and so on).The proposal derives the set of algebraic equations to be solved to find the first-order Taylor expansion to the policy functions using a perturbation. Next, it shows how the traditional approach, based on singular value decomposition (SVD) algorithms and used in the constant parameter case, does not work in the case of MSRE models. Instead, the proposal uses a Gröbner basis method. Then, it studies how to check for determinacy and, as an example, it solves a business cycle model with nominal rigidities. Finally, the proposal points out how the perturbation approach also allows us to find higher-order approximations to the solution of MSRE models. Policy-making institutions in the U.S. and abroad can apply the methods presented in this proposal. For instance, the Federal Reserve Board and several regional Federal Reserve Banks, the International Monetary Fund, the European Central Bank, the Bank of England, and the central banks of Austria, Canada, Germany, Italy, Japan, Spain, and Sweden (just to name a few) are actively formulating and estimating dynamic macroeconomic models for policy analysis and forecasting that can benefit from the type of extensions presented in the proposal. Moreover, the economics profession is accumulating evidence of the good forecasting performance of this class of models, even when compared with judgmental predictions from staff economists. 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 to develop more flexible models that will contribute to the implementation of an effective public policy in the U.S. Finally, the development of new computation techniques has potential applications in other fields of economics (such as international economics, industrial organization, or labor economics), and other social sciences where researchers want to solve and estimate dynamic models using flexible, yet powerful tools.
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依托单位:
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