Polito, V. and Zhang, Y. (2022) Tackling large outliers in macroeconomic data with vector artificial neural network autoregression. Working Paper. Sheffield Economic Research Paper Series, 2022004 (2022004). Department of Economics, University of Sheffield ISSN 1749-8368
Abstract
We develop a regime switching vector autoregression where artificial neural networks drive time variation in the coefficients of the conditional mean of the endogenous variables and the variance covariance matrix of the disturbances. The model is equipped with a stability constraint to ensure non-explosive dynamics. As such, it is employable to account for nonlinearity in macroeconomic dynamics not only during typical business cycles but also in a wide range of extreme events, like deep recessions and strong expansions. The methodology is put to the test using aggregate data for the United States that include the abnormal realizations during the recent Covid-19 pandemic. The model delivers plausible and stable structural inference, and accurate out-of-sample forecasts. This performance compares favourably against a number of alternative methodologies recently proposed to deal with large outliers in macroeconomic data caused by the pandemic.
Metadata
Item Type: | Monograph |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Author(s). For reuse permissions, please contact the Author(s). |
Keywords: | Nonlinear time series; Regime switching models; Extreme events; Covid-19; Macroeconomic forecasting |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Department of Economics (Sheffield) > Sheffield Economics Research Papers Series The University of Sheffield > Faculty of Social Sciences (Sheffield) > Department of Economics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Apr 2022 11:35 |
Last Modified: | 21 Nov 2022 13:19 |
Published Version: | https://www.sheffield.ac.uk/economics/research/ser... |
Status: | Published |
Publisher: | Department of Economics, University of Sheffield |
Series Name: | Sheffield Economic Research Paper Series |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:185767 |