Tackling Large Outliers in Macroeconomic Data with Vector Artificial Neural Network Autoregression

Vito Polito and Yunyi Zhang ()
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Yunyi Zhang: Xiamen University, China

No 2022004, Working Papers from The University of Sheffield, Department of Economics

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.

Keywords: Tax avoidance; Nonlinear time series; Regime switching models; Extreme events; Covid-19; Macroeconomic forecasting (search for similar items in EconPapers)
JEL-codes: C45 C5 E37 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2022-03
New Economics Papers: this item is included in nep-big, nep-cmp, nep-mac and nep-rmg
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https://www.sheffield.ac.uk/economics/research/serps First version, March 2022 (application/pdf)

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Working Paper: Tackling Large Outliers in Macroeconomic Data with Vector Artificial Neural Network Autoregression (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:shf:wpaper:2022004

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