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Using Natural Language Processing to Measure COVID19-Induced Economic Policy Uncertainty for Canada and the US*

Shafiullah Qureshi (), Ba Chu (), Fanny S. Demers () and Michel Demers ()
Additional contact information
Shafiullah Qureshi: Department of Economics, Carleton University, https://carleton.ca/economics/people/qureshi-shafiullah/
Ba Chu: Department of Economics, Carleton University, https://carleton.ca/economics/people/chu-ba-m/
Fanny S. Demers: Department of Economics, Carleton University, https://carleton.ca/economics/people/demers-fanny-s/
Michel Demers: Department of Economics, Carleton University, https://carleton.ca/economics/people/demers-michel/

No 22-01, Carleton Economic Papers from Carleton University, Department of Economics

Abstract: We develop an economic policy uncertainty (EPU) index for Canada and the US using natural language processing (NLP) methods. Our EPU-NLP index is based on an application of several algorithms, including a rapid automatic keyword extraction algorithm (RAKE), a combination of the RoBERTa and the SentenceBERT algorithms, a PyLucene search engine, and the GrapeNLP local grammar engine. Classification-

Pages: 23 pages
Date: 2022-01-18
New Economics Papers: this item is included in nep-big and nep-cmp
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Published: Carleton Economics Working Papers

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