摘要翻译:
在本文中,我们考虑了状态空间模型中未观测分量的估计,使用动态因子方法来结合来自高维数据源的辅助信息。我们将荷兰统计局的方法应用于失业估计,后者使用多元状态空间模型,利用劳动力调查(LFS)观察到的系列,产生失业的月度数字。我们扩展了这个模型,包括谷歌关于求职和经济不确定性的趋势的辅助系列,以及索赔人数,部分是在更高的频率上观察到的。我们的因子模型允许对感兴趣的变量进行临近预报,在LFS数据可用之前实时提供可靠的失业估计。
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英文标题:
《A dynamic factor model approach to incorporate Big Data in state space
models for official statistics》
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作者:
Caterina Schiavoni, Franz Palm, Stephan Smeekes, Jan van den Brakel
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最新提交年份:
2020
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分类信息:
一级分类:Economics 经济学
二级分类:Econometrics 计量经济学
分类描述:Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论,微观计量经济学,宏观计量经济学,通过新方法发现的经济关系的实证内容,统计推论应用于经济数据的方法论方面。
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一级分类:Statistics 统计学
二级分类:Methodology 方法论
分类描述:Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计,调查,模型选择,多重检验,多元方法,信号和图像处理,时间序列,平滑,空间统计,生存分析,非参数和半参数方法
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英文摘要:
In this paper we consider estimation of unobserved components in state space models using a dynamic factor approach to incorporate auxiliary information from high-dimensional data sources. We apply the methodology to unemployment estimation as done by Statistics Netherlands, who uses a multivariate state space model to produce monthly figures for the unemployment using series observed with the labour force survey (LFS). We extend the model by including auxiliary series of Google Trends about job-search and economic uncertainty, and claimant counts, partially observed at higher frequencies. Our factor model allows for nowcasting the variable of interest, providing reliable unemployment estimates in real-time before LFS data become available.
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PDF链接:
https://arxiv.org/pdf/1901.11355