Can Alternative Data Improve the Accuracy of Dynamic Factor Model Nowcasts
Can Alternative Data Improve the Accuracy of Dynamic Factor Model Nowcasts
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替代数据能否提高动态因子模型临近预报的准确性
DOI：
10.17863/cam.62843
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Cristea
中科院分区：
文献类型：
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作者：
Radu Gabriel;Cristea
我们采用标准动态因子模型对欧元区真实的GDP增长进行预测，并测试增加几个扩展如何提高预测精度。我们用高频替代数据扩展了模型的信息集，并修改了一些传统变量的考虑方式。随后，我们用软数据、劳动力和金融市场、实时数据和经济供给侧的模块丰富了要素结构。因此，我们丰富的临近预测准确地检测到了2020年第一季度的经济下滑，并正确地表明了由于COVID-19冲击，第二季度的经济萎缩将进一步加剧。从几个真正的和伪实时的样本外预测评估练习的结果显示，临近预报精度的收益，衡量的均方根误差或柯伊伯分数。我们还表明，这些收益源于新的数据来源和因素结构。虽然我们的模型在正常时期的表现是适度的，但在严重压力的时候是有意义的。
We take the standard dynamic factor model for euro area real GDP growth nowcasting and test how adding several extensions improves forecasting precision. We expand the model's information set with high frequency alternative data and amend how some of the traditional variables are considered. Subsequently, we enrich the factors structure with blocks for soft data, labour and financial markets, real-time data and the supply side of the economy. As a result, our enriched nowcast has accurately detected the downturn in Q1-2020 and has correctly indicated further and steeper contraction in Q2 due to the COVID-19 shock. Results from several genuine and pseudo real-time out-of-sample forecast evaluation exercises show nowcasting precision gains, as measured by the root mean squared error or Kuiper's score. We also show these gains stem from the novel data sources and factors structure. While our model's outperformance is modest in normal times, it is meaningful in times of severe stress.
DOI：
10.2139/ssrn.317983
发表时间：
2002-04
期刊：
Penn Institute for Economic Research (PIER) Working Paper Series
影响因子：
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作者：
R. Mariano;Yasutomo Murasawa
通讯作者：
R. Mariano;Yasutomo Murasawa