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• W2892291469 abstract "Vector autoregressions (VARs) with informative steady-state priors are standard forecasting tools in empirical macroeconomics. This study proposes (i) an adaptive hierarchical normal-gamma prior on steady states, (ii) a time-varying steady-state specification which accounts for structural breaks in the unconditional mean, and (iii) a generalization of steady-state VARs with fat-tailed and heteroskedastic error terms. Empirical analysis, based on a real-time dataset of 14 macroeconomic variables, shows that, overall, the hierarchical steady-state specifications materially improve out-of-sample forecasting for forecasting horizons longer than 1 year, while the time-varying specifications generate superior forecasts for variables with significant changes in their unconditional mean." @default.
• W2892291469 created "2018-09-27" @default.
• W2892291469 creator A5069115216 @default.
• W2892291469 date "2018-10-07" @default.
• W2892291469 modified "2023-09-24" @default.
• W2892291469 title "Steady‐state modeling and macroeconomic forecasting quality" @default.
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• W2892291469 doi "https://doi.org/10.1002/jae.2657" @default.
• W2892291469 hasPublicationYear "2018" @default.
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