跳到正文
← 返回论文列表与筛选
阅读说明

本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。

DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。

引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。

关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。

分类依据与纠错

AI 根据所提供的论文材料归类:摘要研究多变量时间序列的 VARMA 建模,并明确比较宏观货币政策模型的脉冲响应与样本外预测,对应这三个方向。

反馈分类问题

Practical Methods for Modeling Weak VARMA Processes: Identification, Estimation and Specification With a Macroeconomic Application

弱 VARMA 过程的实用建模:识别、估计、模型设定与宏观应用

正在核对关联版本…

代码、数据与补充材料

正在核对对应版本的资源…

关注这篇论文的作者
Jean-Marie Dufour
Denis Pelletier

弱 VARMA 过程的实用建模:识别、估计、模型设定与宏观应用

研究问题
对创新项不相关但可存在依赖的弱 VARMA 过程,如何构造可识别、易估计和可定阶的模型,并用于宏观响应与预测?
方法与思路
作者提出对角 MA 方程形式与最终 MA 方程形式,用线性回归估计参数,以修正信息准则定阶,并开展模拟和宏观应用。
材料陈述的贡献
摘要称在不相关、强混合创新条件下给出估计的渐近性质和一致定阶结果,并在所考察应用中报告响应估计与预测优势。
阅读关注点
应核对两种表示的识别约束、回归估计步骤、信息准则的一致性条件,以及脉冲响应识别和样本外预测的比较设计。
阅读边界
摘要未说明完整识别约束、混合与矩条件及预测评估细节;所述优于 VAR 的结果限于文中比较,不能直接外推。

原文摘要

We consider the problem of developing practical methods for modelling weak VARMA processes. We first propose new identified VARMA representations, the diagonal MA equation form and the final MA equation form, where the MA operator is either diagonal or scalar. Both these representations have the important feature that they constitute relatively simple modifications of a VAR model (in contrast with the echelon representation). Second, for estimating VARMA models, we develop computationally simple methods which only require linear regressions. The asymptotic properties of the estimator are derived under weak hypotheses on the innovations (uncorrelated and strong mixing), in order to broaden the class of models to which it can be applied. Third, we present a modified information criterion which yields consistent estimates of the orders under the proposed representations. The estimation methods are studied by simulation. To demonstrate the importance of using VARMA models to study multivariate time series, we compare the impulse-response functions and the out-of-sample forecasts generated by VARMA and VAR models. The proposed methodology is applied to a six-variable macroeconomic model of monetary policy, based on the U.S. monthly data over the period 1962–1996. The results demonstrate the advantages of using the VARMA methodology for impulse response estimation and forecasting, in contrast with standard VAR models.

笔记与分组
相关领域论文