Nowcasting Economic Activity with Mobility Data

Matsumura, Kohei; Oh, Yusuke; Sugo, Tomohiro; Takahashi, Koji

doi:10.2139/ssrn.4406104

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Second, models estimated in this paper combine standard high‐frequency data, including official statistics, with new data sources, which are increasingly becoming available and being tested as a source of more timely information on activity in services. For example, Matsumura et al (2021) use GPS mobility data from mobile applications to measure activities in amusement parks, shopping centres, and food services in Japan. They found that those data are particularly useful to nowcast activity in services industries in real time.…”

Section: Literaturementioning

confidence: 99%

Nowcasting services trade for the G7 economies

Jaax,

Mourougane,

Gonzales

2023

World Economy

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The growing importance of services trade in the global economy contrasts with the scarcity of timely data on this part of international trade. This paper develops models to nowcast aggregate services imports and exports, as measured by monthly services trade data, for the G7 countries. The methodology relies on machine‐learning techniques and dynamic factor models and combines traditional high‐frequency data with the use of Google Trends search data. The estimated services trade nowcasting models display a higher out‐of‐sample predictive power than a simple benchmark model. However, there does not seem to be one approach that outperforms other model specifications. Rather, a weighted average of the best models, combining machine‐learning with dynamic factor models, seems to be a promising avenue. The best models improve one step ahead predictive performance relative to a simple benchmark by 30%–35% on average across the G7 countries and across trade flows. Models capture approximately 67% of the fall in services exports following the COVID‐19 shock and 60% of the fall in imports on average across G7 economies.

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Section: Literaturementioning

confidence: 99%