Forecasting Macroeconomic Variables Under Model Instability
比较了两种参数不稳定建模方法（小频繁变化与大罕见变化）在预测美国GDP增长和通胀时的表现，发现允许参数不稳定的模型能生成更准确的密度预测，但点预测未优于常数参数模型。
We compare different approaches to accounting for parameter instability in the context of macroeconomic forecasting models that assume either small, frequent changes versus models whose parameters exhibit large, rare changes. An empirical out-of-sample forecasting exercise for U.S. gross domestic product (GDP) growth and inflation suggests that models that allow for parameter instability generate more accurate density forecasts than constant-parameter models although they fail to produce better point forecasts. Model combinations deliver similar gains in predictive performance although they fail to improve on the predictive accuracy of the single best model, which is a specification that allows for time-varying parameters and stochastic volatility. Supplementary materials for this article are available online.