We introduce a flexible nonparametric technique that can be used to select weights in a forecast-combining regression. We perform a Monte Carlo study that evaluates the p[...]

This paper describes a new test for evaluating conditional density functions that remains valid when the data are time-dependent and that is therefore applicable to forec[...]

This paper presents a new test for evaluating conditional density functions for time-series data, thereby being applicable to forecasting problems. We show that the test [...]

We propose a new test for a multivariate parametric conditional distribution of a vector of variables yt given a conditional vector xt. The proposed test is shown to have[...]

We propose a new test for a multivariate parametric conditional distribution of a vector of variables yt given a conditional vector xt. The proposed test is shown to have[...]

Knight, Li, and Yuan (1999) have developed a non-parametric procedure for estimating diffusion functions in a general multivariate diffusion process. We apply this estima[...]

This paper evaluates linear and non-linear forecast-combination methods. Among the non-linear methods, we propose a nonparametric kernel-regression weighting approach tha[...]

This paper examines empirically the impact of financial stress on the transmission of monetary policy shocks in Canada. The model used is a threshold vector autoregressio[...]

Based on a new approach for measuring the comovements between stock market returns, we provide a new test for the null hypothesis of symmetric comovements in the sense th[...]

Based on a new approach for measuring the comovements between stock market returns, we provide a nonparametric test for asymmetric comovements in the sense that stock mar[...]