VARs. To address these issues, we propose VAR models with outlier-augmented stochastic volatility (SV) that combine … the pandemic period, as well as for earlier subsamples of relatively high volatility. In historical forecasting, outlier …

VARs are a popular tool for forecasting and structural analysis, but ill-suited to handle occasionally binding … constraints, like the effective lower bound on nominal interest rates. We extend the VAR framework by modeling interest rates as … including both. In comparison to a standard VAR, shadow-rate VARs generate superior predictions for short- and long …

specification and a specification with a common factor in volatility that enters the VAR's conditional mean. Even though the one … comparably to quantile regression for estimating and forecasting tail risks, complementing BVARs' established performance for … forecasting and structural analysis …

Recent work suggests VAR models of output, inflation, and interest rates may be prone to instabilities. In the face of … such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from … a VAR. The uncertainty inherent in any single representation of instability could mean that combining forecasts from a …

Interest rate data are an important element of macroeconomic forecasting. Projections of future interest rates are not … only an important product themselves, but also typically matter for forecasting other macroeconomic and financial variables …. A popular class of forecasting models is linear vector autoregressions (VARs) that include shorter- and longer …

stochastic volatility (SV) of VAR residuals. Specifically, we propose VAR models with outlier-augmented SV that combine …