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 performance of the proposed technique along with other linear and nonlinear forecast-combining procedures. The simulation...

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 an asymptotic normal distribution under the null hypothesis, while being consistent for all fixed alternatives, and having...

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 performance of the proposed technique along with other linear and nonlinear forecast-combining procedures. The simulation...

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 forecasting problems. We show that the test statistic is asymptotically distributed standard normal under the null hypothesis, and...

This paper evaluates linear and non-linear forecast-combination methods. Among the non-linear methods, we propose a nonparametric kernel-regression weighting approach that allows maximum flexibility of the weighting parameters. A Monte Carlo simulation study is performed to compare the...

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 an asymptotic normal distribution under the null hypothesis, while being consistent for all fixed alternatives, and having...

The New Keynesian Phillips Curve is at the center of two raging empirical debates. First, how can purely forward looking pricing account for the observed persistence in aggregate inflation. Second, price-setting responds to movements in marginal costs, which should therefore be the driving force...