.png){kind=icon}
返回
Now-casting inflation using high frequency data
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijforecast.2012.12.003.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper proposes a methodology for now-casting and forecasting inflation using data with a sampling frequency which is higher than monthly. The data are modeled as a trading day frequency factor model, with missing observations in a state space representation. For the estimation we adopt the methodology proposed by Banbura and Modugno (2010). In contrast to other existing approaches, the methodology used in this paper has the advantage of modeling all data within a single unified framework which allows one to disentangle the model-based news from each data release and subsequently to assess its impact on the forecast revision. The results show that the inclusion of high frequency data on energy and raw material prices in our data set contributes considerably to the gradual improvement of the model performance. As long as these data sources are included in our data set, the inclusion of financial variables does not make any considerable improvement to the now-casting accuracy. (C) 2013 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keyword:
Factor models
Forecasting
Inflation
Mixed frequencies
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.1
论文数:
3.1K
被引数:
9.9K
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏Serotonin Uptake Sites and Serotonin Receptors Are Altered in the Limbic System of Schizophrenics
OAAI
OAAI分享收藏 没有更多内容
没有更多内容