Measuring Uncertainty at the Regional Level Using Newspaper Text

Christopher Rauh

No 09-2019, Cahiers de recherche from Centre interuniversitaire de recherche en économie quantitative, CIREQ

Abstract: In this paper I present a methodology to provide uncertainty measures at the regional level in real time using the full bandwidth of news. In order to do so I download vast amounts of newspaper articles, summarize these into topics using unsupervised machine learning, and then show that the resulting topics foreshadow fluctuations in economic indicators. Given large regional disparities in economic performance and trends within countries, it is particularly important to have regional measures for a policymaker to tailor policy responses. I use a vector-autoregression model for the case of Canada, a large and diverse country, to show that the generated topics are significantly related to movements in economic performance indicators, inflation, and the unemployment rate at the national and provincial level. Evidence is provided that a composite index of the generated diverse topics can serve as a measure of uncertainty. Moreover, I show that some topics are general enough to have homogenous associations across provinces, while others are specific to fluctuations in certain regions.

Keywords: machine learning; latent dirichlet allocation; newspaper text; economic uncertainty; topic model; Canada (search for similar items in EconPapers)
Date: 2019-09
New Economics Papers: this item is included in nep-big, nep-cmp, nep-geo and nep-ure
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Working Paper: Measuring uncertainty at the regional level using newspaper text (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:mtl:montec:09-2019

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