This paper examines the importance of realized volatility in bond yield density prediction. We incorporate realized volatility into a Dynamic Nelson-Siegel (DNS) model with stochastic volatility and evaluate its predictive performance on US bond yield data. When compared to popular...

This paper proposes asymptotically optimal tests for unstable parameter process under the feasible circumstance that the researcher has little information about the unstable parameter process and the error distribution, and suggests conditions under which the knowledge of those processes does...

This paper introduces nonlinearity and a structural break to the US forward-looking Taylor rule with a stock price gap, thereby alleviating the robustness problem that the linear Taylor rule is sensitive to minor changes of the sample period since 1991. The path of the time-varying inflation...

Quantile regression (QR) models have been increasingly employed in many applied areas in economics. At the early stage, applications in the quantile regression literature have usually used cross-sectional data, but the recent development has seen an increase in the use of quantile regression in...

We propose point forecast accuracy measures based directly on distance of the forecast-error c.d.f. from the unit step function at 0 (\stochastic error distance," or SED). We provide a precise characterization of the relationship between SED and standard predictive loss functions, showing that...

We explore the evaluation (ranking) of point forecasts by a “stochastic loss distance” (SLD) criterion, under which we prefer forecasts with loss distributions F(L(e)) “close” to the unit step function at 0. We show that, surprisingly, ranking by SLD corresponds to ranking by expected loss.

We propose and explore several related ways of reducing reliance of point forecast accuracy evaluation on expected loss, E(L(e)), where e is forecast error. Our central approach dispenses with the loss function entirely, instead using a \stochastic error divergence" (SED) accuracy measure based...