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Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs
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This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of extreme observations, for instance those produced by the COVID-19 pandemic of 2020. This is due to their flexibility and ability to model outliers. In an application involving four major euro area countries, we find substantial improvements in nowcasting performance relative to a linear mixed frequency VAR.
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Forecasting Thai inflation from univariate Bayesian regression perspective
Simple Bayesian shrinkage regressions with up to 56 predictors often improve one-step-ahead Thai inflation forecasts, but the paper's evidence is internally inconsistent and does not support the broader claims.
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