Forecasting US output growth with large information sets

dc.contributor.authorSalisu, Afees A.
dc.contributor.authorNdako, Umar Bida
dc.contributor.authorGupta, Rangan
dc.contributor.emailrangan.gupta@up.ac.zaen_US
dc.date.accessioned2022-10-25T08:07:20Z
dc.date.available2022-10-25T08:07:20Z
dc.date.issued2021
dc.description.abstractWe forecast US output growth using an array of both Classical and Bayesian models including the recently developed Dynamic Variable Selection prior with Variational Bayes [DVSVB] of Koop and Korobilis (2020). We accommodate over 300 predictors that are incrementally captured from 5 factors, 60 factors to over 300 factors covering relevant economic agents. For robustness, we allow for both constant and time varying coefficients as well as alternative proxies for output growth. Using data covering 1960:Q1 to 2018:Q4, our results consistently support the use of high-dimensional models when forecasting US output growth regardless of the choice of forecast measure. For the density forecast of real GDP growth in particular, the results favour the DVSVB and time varying parameter assumption.en_US
dc.description.departmentEconomicsen_US
dc.description.librarianam2022en_US
dc.description.urihttps://refpress.orgen_US
dc.identifier.citationSalisu, A.A., Ndako, U.B., Gupta, R. 2021, 'Forecasting US output growth with large information sets', Review of Economics and Finance, vol. 19, pp. 13-16, doi : 10.55365/1923.X2021.19.02.en_US
dc.identifier.issn1923-7529 (print)
dc.identifier.issn1923-8401 (online)
dc.identifier.other10.55365/1923.X2021.19.02
dc.identifier.urihttps://repository.up.ac.za/handle/2263/87929
dc.language.isoenen_US
dc.publisherREF Pressen_US
dc.rights© 2020– All Rights Reserved. This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License.en_US
dc.subjectUS output growthen_US
dc.subjectHigh-dimensional modelsen_US
dc.subjectForecast evaluationen_US
dc.subjectUnited States (US)en_US
dc.subjectDynamic variable selection prior with variational Bayes (DVSVB)en_US
dc.titleForecasting US output growth with large information setsen_US
dc.typeArticleen_US

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Salisu_Forecasting_2021.pdf
Size:
190.1 KB
Format:
Adobe Portable Document Format
Description:
Article

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.75 KB
Format:
Item-specific license agreed upon to submission
Description: