Estimation bias and bias correction in reduced rank autoregressions
研究了降秩向量自回归模型中最大似然估计的有限样本偏差，提出了两种基于模拟的偏差校正方法，并通过美国宏观经济时间序列数据验证了其有效性。
This paper characterizes the finite-sample bias of the maximum likelihood estimator (MLE) in a reduced rank vector autoregression and suggests two simulation-based bias corrections. One is a simple bootstrap implementation that approximates the bias at the MLE. The other is an iterative root-finding algorithm implemented using stochastic approximation methods. Both algorithms are shown to be improvements over the MLE, measured in terms of mean square error and mean absolute deviation. An illustration to US macroeconomic time series is given.