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Faster socieoeconomic indicators using novel data sources
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Miller, Sam (2022) Faster socieoeconomic indicators using novel data sources. PhD thesis, University of Warwick.
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Official URL: http://webcat.warwick.ac.uk/record=b3878433
Abstract
Policymakers require up-to-date statistics to make good decisions. Most official statistics in economics and public health are released only after a significant delay. The goal of “nowcasting" (a combination of the words “now" and “forecasting") is to estimate these statistics before their official release. Better nowcasts would help policymakers respond to rapidly developing crises in a range of domains. The recent Covid-19 crisis has highlighted this issue: it is extremely challenging to make decisions in crisis without knowledge of either the current state of the economy or the incidence of disease in the population. Recent technological advances mean we now generate real-time data simply by going about our lives. This thesis shows how we can use novel data sources to improve nowcasts in economics and public health. We highlight how we are no longer constrained to traditional data sources, such as surveys. We first investigate whether high-frequency aircraft location data can generate faster GDP estimates. We also show that this dataset can help improve estimates of airport performance, particularly at the onset of the Covid-19 crisis. We next use a novel combination of Wikipedia page views and data scraped from online “darknet" drug markets to nowcast illicit drug demand. Better statistics on drug markets would be highly valuable to policymakers in both economics and public health. Finally, we use data from Google Trends to nowcast the incidence of chikungunya in Rio de Janeiro. Official disease data is delivered with long and variable delays. We show that including real-time Google Trends data allows earlier detection of epidemics. This thesis finds evidence that novel data sources can improve the speed and accuracy of official statistics in a range of domains. As the variety of novel data sources keeps growing, these may give policymakers more complete, real-time information when making crucial decisions.
Item Type: | Thesis (PhD) | ||||
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Subjects: | H Social Sciences > H Social Sciences (General) H Social Sciences > HA Statistics H Social Sciences > HB Economic Theory Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Library of Congress Subject Headings (LCSH): | Statistical methods -- Technological innovations, Real-time data processing, Economics -- Statistical methods, Economic indicators, Economic forecasting, Policy sciences, Information services, Public health -- Statistical methods | ||||
Official Date: | June 2022 | ||||
Dates: |
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Institution: | University of Warwick | ||||
Theses Department: | Warwick Business School | ||||
Thesis Type: | PhD | ||||
Publication Status: | Unpublished | ||||
Supervisor(s)/Advisor: | Moat, Suzy ; Preis, Tobias, 1981- | ||||
Sponsors: | Alan Turing Institute ; Engineering and Physical Sciences Research Council ; Office for National Statistics | ||||
Format of File: | |||||
Extent: | vi, 134 pages : illustrations (colour), charts | ||||
Language: | eng |
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