innovations have been well investigated. In contrast, the forecasting implications of specifying UC models with different state … studies adopting UC models for forecasting purposes. Four correlation structures for errors are entertained: orthogonal … and their connection with forecasting are discussed within a Bayesian framework. As perfectly correlated innovations …

This paper revisits inflation forecasting using reduced-form Phillips curve forecasts, that is, inflation forecasts … evidence of autonomous variance breaks and inflation gap persistence. Through a real-time out-of-sample forecasting exercise … quarterly inflation relative to an extended range of forecasting models that are typically used in the literature. -- Inflation …

This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting in ation … risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions for forecasting … approach accessible and empirically relevant for forecasting, we derive an efficient Gibbs sampler by transforming the state …

This paper revisits the accuracy of inflation forecasting using activity and expectations variables. We apply Bayesian … forecasts, especially for the post-1984 period. Our framework is especially useful when forecasting, in real-time, the …