Tests of Rank in Reduced Rank Regression Models
通过模拟实验评估降秩回归模型中秩的渐近检验及其自助法版本的表现，发现自助法显著改进。蒙特卡洛比较显示降秩VAR模型预测优于无限制VAR，并应用于英国经济领先指标建模。
There has recently been renewed research interest in the development of tests of the rank of a matrix. This article evaluates the performance of some asymptotic tests of rank determination in reduced rank regression models together with bootstrapped versions through simulation experiments. The bootstrapped procedures significantly improve on the performance of the corresponding asymptotic tests. The article also presents a Monte Carlo exercise comparing the forecasting performance of reduced rank and unrestricted vector autoregressive (VAR) models in which the former appear superior. The tests of rank considered here are then applied to construct reduced rank VAR models for leading indicators of U.K. economic activity. These more parsimonious multivariate representations display an improvement in forecasting performance over that of unrestricted VAR models.