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Title: Tackling large outliers in macroeconomic data with vector artificial neural network autoregression Authors:  Yunyi Zhang - Xiamen University (China) [presenting]
Abstract: A regime-switching vector autoregression is developed 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 changes 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 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 pandemics.