日期精度：到日 0 篇 · 到月 9 篇 · 到年 0 篇
向众数回归
We propose a semi-parametric mode regression estimator for the case in which the dependent variable has a continuous conditional density with a well-defined global mode. The estimator is semi-parametric in that the conditional mode is specified as a parametric function, but only mild assumptions are made about the nature of the conditional density of interest. We show that the proposed estimator is consistent and has a tractable asymptotic distribution.
界限置信区域的构造
We modify the procedure for constructing exact bounds confidence regions introduced by Dufour (1990, Econometrica 58, 475–494) so as to drop the requirement that the confidence regions for the nuisance parameters are marginal with respect to the parameters of interest, i.e., that they are the same for all values of the parameters of interest. We illustrate this modified procedure with an application to a dependent variable heteroskedasticity model and, using a Monte Carlo…
样本量与预测期同步增长时近单位根 AR(1) 模型的预测误差
We develop asymptotic approximations to the distribution of forecast errors from an estimated AR(1) model with no drift when the true process is nearly I (1) and both the forecast horizon and the sample size are allowed to increase at the same rate. We find that the forecast errors are the sums of two components that are asymptotically independent. The first is asymptotically normal whereas the second is asymptotically nonnormal. This throws doubt on the suitability of a…
AR(1) 模型中预测误差的联合分布
Second-order asymptotic expansion approximations to the joint distributions of dynamic forecast errors and of static forecast errors in the stationary Gaussian pure AR(1) model are derived. The approximation to the dynamic forecast errors distribution can be expressed as a multivariate normal distribution with modified mean vector and covariance matrix, thus generalizing the results of Phillips [12]. However, the approximation to the static forecast errors distribution…