Data outliers and Bayesian VARs in the Euro Area

Luis Alvarez and Florens Odendahl ()
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Florens Odendahl: Banco de España

No 2239, Working Papers from Banco de España

Abstract: We propose a method to adjust for data outliers in Bayesian Vector Autoregressions (BVARs), which allows for different outlier magnitudes across variables and rescales the reduced form error terms. We use the method to document several facts about the effect of outliers on estimation and out-of-sample forecasting results using euro area macroeconomic data. First, the COVID-19 pandemic led to large swings in macroeconomic data that distort the BVAR estimation results. Second, these swings can be addressed by rescaling the shocks’ variance. Third, taking into account outliers before 2020 leads to mild improvements in the point forecasts of BVARs for some variables and horizons. However, the density forecast performance considerably deteriorates. Therefore, we recommend taking into account outliers only on pre-specified dates around the onset of the COVID-19 pandemic.

Keywords: COVID-19 pandemic; outliers; Bayesian VARs; forecasting; euro area (search for similar items in EconPapers)
JEL-codes: C11 C32 C51 E37 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2022-11
New Economics Papers: this item is included in nep-des, nep-ecm, nep-eec and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:bde:wpaper:2239

DOI: 10.53479/23552

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