美国通货膨胀的实时密度预报:一种模型组合方法

IF 6.9 2区经济学 Q1 ECONOMICS

International Journal of Forecasting Pub Date : 2023-10-01 DOI:10.1016/j.ijforecast.2022.04.007

Edward S. Knotek II, Saeed Zaman

{"title":"美国通货膨胀的实时密度预报:一种模型组合方法","authors":"Edward S. Knotek II, Saeed Zaman","doi":"10.1016/j.ijforecast.2022.04.007","DOIUrl":null,"url":null,"abstract":"<div><p>We develop a flexible modeling framework to produce density nowcasts for US inflation at a trading-day frequency. Our framework (1) combines individual density nowcasts from three classes of parsimonious mixed-frequency models; (2) adopts a novel flexible treatment in the use of the aggregation function; and (3) permits dynamic model averaging via the use of weights that are updated based on learning from past performance. These features provide density nowcasts that can potentially accommodate non-Gaussian properties. We document the competitive properties of the nowcasts generated from our framework using high-frequency real-time data over the period 2000–2015.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"39 4","pages":"Pages 1736-1760"},"PeriodicalIF":6.9000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Real-time density nowcasts of US inflation: A model combination approach\",\"authors\":\"Edward S. Knotek II, Saeed Zaman\",\"doi\":\"10.1016/j.ijforecast.2022.04.007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>We develop a flexible modeling framework to produce density nowcasts for US inflation at a trading-day frequency. Our framework (1) combines individual density nowcasts from three classes of parsimonious mixed-frequency models; (2) adopts a novel flexible treatment in the use of the aggregation function; and (3) permits dynamic model averaging via the use of weights that are updated based on learning from past performance. These features provide density nowcasts that can potentially accommodate non-Gaussian properties. We document the competitive properties of the nowcasts generated from our framework using high-frequency real-time data over the period 2000–2015.</p></div>\",\"PeriodicalId\":14061,\"journal\":{\"name\":\"International Journal of Forecasting\",\"volume\":\"39 4\",\"pages\":\"Pages 1736-1760\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Forecasting\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0169207022000589\",\"RegionNum\":2,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Forecasting","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169207022000589","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}

引用次数: 0

摘要

我们开发了一个灵活的建模框架，以产生交易日频率的美国通胀密度预测。我们的框架（1）结合了来自三类简约混合频率模型的个体密度nowcast；（2）在使用聚合函数时采用了一种新颖灵活的处理方式；以及（3）允许通过使用基于从过去性能学习而更新的权重来进行动态模型平均。这些特征提供了可以潜在地适应非高斯特性的密度nowcast。我们使用2000-2015年期间的高频实时数据，记录了我们框架生成的即时广播的竞争特性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Real-time density nowcasts of US inflation: A model combination approach

We develop a flexible modeling framework to produce density nowcasts for US inflation at a trading-day frequency. Our framework (1) combines individual density nowcasts from three classes of parsimonious mixed-frequency models; (2) adopts a novel flexible treatment in the use of the aggregation function; and (3) permits dynamic model averaging via the use of weights that are updated based on learning from past performance. These features provide density nowcasts that can potentially accommodate non-Gaussian properties. We document the competitive properties of the nowcasts generated from our framework using high-frequency real-time data over the period 2000–2015.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

International Journal of Forecasting Multiple-

CiteScore

17.10

自引率

11.40%

发文量

189

审稿时长

77 days

期刊介绍： The International Journal of Forecasting is a leading journal in its field that publishes high quality refereed papers. It aims to bridge the gap between theory and practice, making forecasting useful and relevant for decision and policy makers. The journal places strong emphasis on empirical studies, evaluation activities, implementation research, and improving the practice of forecasting. It welcomes various points of view and encourages debate to find solutions to field-related problems. The journal is the official publication of the International Institute of Forecasters (IIF) and is indexed in Sociological Abstracts, Journal of Economic Literature, Statistical Theory and Method Abstracts, INSPEC, Current Contents, UMI Data Courier, RePEc, Academic Journal Guide, CIS, IAOR, and Social Sciences Citation Index.