Shrinkage for Vector Autoregressions and Impulse Response Estimation

矢量自回归和脉冲响应估计的收缩

• 批准号: 1656123
• 负责人: Bruce Hansen
• 金额: $ 23.64万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1656123&HistoricalAwards=false
• 关键词: Shrinkage; Vector; Autoregressions; Impulse; Response

The core of Macroeconomic policy analysis is understanding the impact of unexpected events, news, and variation on core economic variables including GDP, inflation, wages, investment, and employment. Applied macroeconomic research focuses on estimation of these impacts known as "impulse response functions". Current estimation methods are less precise than desirable, and are difficult to implement with a large number of variables. This project develops new methods which produce sharper and more precise estimates of these effects, allowing for more precise understanding of the macro economy and economic policy. The methods are based on combination (or ensembles) of simpler methods. The new method can be much more precise than existing simpler methods.This project explores impulse response function (IRF) estimation in vector auto-regressions (VARs) by model combination. Estimates from lower-dimensional models (VARs and ARs of lower order) will be combined by standard model averaging methods. The IRF is a non-linear function of the VAR coefficients. The investigator develops a large-sample (asymptotic) approximation to the distribution of the combination IRF. Using this asymptotic approximation, this research calculates the approximate mean-squared error (MSE) of the combination IRF, and shows how to estimate the MSE using an appropriate information criterion which is similar to a Mallows criterion or Focused information criterion. The combination weights can then be selected to minimize this criterion function, resulting in a practical combination estimator. The goal of the project is to study the statistical properties of this combination method and extend its application to high dimensional contexts.

宏观经济政策分析的核心是了解突发事件，新闻和变化对核心经济变量的影响，包括GDP，通货膨胀，工资，投资和就业。应用宏观经济学研究的重点是估计这些影响，称为“脉冲响应函数”。目前的估计方法不如期望的精确，并且难以用大量的变量来实现。该项目开发了新的方法，可以更准确地估计这些影响，从而更准确地了解宏观经济和经济政策。这些方法基于较简单方法的组合（或集合）。本计画主要探讨向量自回归模型中之脉冲响应函数估计。将通过标准模型平均法合并低维模型（较低阶的VAR和AR）的估计值。IRF是VAR系数的非线性函数。研究者开发联合IRF分布的大样本（渐近）近似值。使用这种渐近近似，本研究计算的组合IRF的近似均方误差（MSE），并显示如何估计MSE使用适当的信息准则，这是类似于马洛斯准则或集中信息准则。然后可以选择组合权重以最小化该准则函数，从而得到实用的组合估计器。该项目的目标是研究这种组合方法的统计特性，并将其应用扩展到高维环境。

项目成果

Inference for Iterated GMM Under Misspecification

错误指定下迭代 GMM 的推理

• DOI: 10.3982/ecta16274
• 发表时间: 2021
• 期刊: Econometrica
• 影响因子: 6.1
• 作者: Hansen, Bruce E.;Lee, Seojeong
• 通讯作者: Lee, Seojeong

Stein-like 2SLS estimator

类 Stein 2SLS 估计器

• DOI: 10.1080/07474938.2017.1307579
• 发表时间: 2017
• 期刊: Econometric Reviews
• 影响因子: 1.2
• 作者: Hansen, Bruce E.

Asymptotic theory for clustered samples

聚类样本的渐近理论

• DOI: 10.1016/j.jeconom.2019.02.001
• 发表时间: 2019
• 期刊: Journal of Econometrics
• 影响因子: 6.3
• 作者: Hansen, Bruce E.;Lee, Seojeong
• 通讯作者: Lee, Seojeong

Johansen’s Reduced Rank Estimator Is GMM

Johansen 的降阶估计量是 GMM

• DOI: 10.3390/econometrics6020026
• 发表时间: 2018
• 期刊: Econometrics
• 影响因子: 1.5
• 作者: Hansen, Bruce

Time series econometrics for the 21st century

21世纪的时间序列计量经济学

• DOI: 10.1080/00220485.2017.1320610
• 发表时间: 2017
• 期刊: The Journal of Economic Education
• 作者: Hansen, Bruce E.