Steady‐state modeling and macroeconomic forecasting quality
改进了向量自回归模型的稳态先验设定，提出自适应分层先验、时变稳态和厚尾异方差误差项，基于14个宏观变量的实时数据发现这些改进能显著提升一年以上预测期的样本外预测质量。
Summary 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.