 Can Alternative Data Improve the Accuracy of Dynamic Factor Model Nowcasts?Radu Gabriel Cristea Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge Abstract: 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. Keywords: nowcasting; dynamic factor model; Kalman filter; real-time high frequency alternative data; Google econometrics; COVID-19; euro area macroeconomics (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:20108 Access Statistics for this paper More papers in Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge |
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