Name:
Economic uncertainty at risk(R ...
Embargo:
2022-12-03
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359.0Kb
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Microsoft Word 2007
Authors
Apergis, NicholasAffiliation
University of DerbyIssue Date
2020-12-03
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The goal of this paper is to employ a relatively new methodological approach to extract quantile-based economic policy uncertainty risk forecasts using the Quantile Autoregressive Distributed Lag Mixed-Frequency Data Sampling (QADL-MIDAS) regression model recommended by Ghysels and Iania (2018). This type of modelling delivers better quantile forecasts at various forecasting horizons. The forecasting results not only imply that the risk measure of economic policy uncertainty measure is linked to the future evolution of the index itself, but also it help constructing explicitly EPU risk measures, which are used to identify what drives such risk policy measures, especially across certain sub-sample periods associated with major global events, such as the collapse of the Lehman Brothers, the Trump’s election, and the trade-war tensions between the US and China. The findings offer a new empirical perspective to the existing economic policy uncertainty literature, documenting that special world events carry a strong informational content as being a primary key to understand the dynamics of the economic policy tails.Citation
Apergis, N. (2020). 'Evaluating tail risks for the US economic policy uncertainty'. International Journal of Finance and Economics, pp. 1-40.Publisher
WileyJournal
International Journal of Finance and EconomicsDOI
10.1002/ijfe.2354 PDFPDFAdditional Links
https://onlinelibrary.wiley.com/doi/epdf/10.1002/ijfe.2354Type
ArticleLanguage
enEISSN
1099-1158ae974a485f413a2113503eed53cd6c53
10.1002/ijfe.2354 PDFPDF
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