GLS estimation and confidence sets for the date of a single break in models with trends
提出一种可行广义最小二乘估计量来估计水平或趋势的结构断点日期，基于逆自协方差矩阵的一致估计，并构建基于Wald检验和似然比检验的置信区间/集，模拟显示新估计量提高了断点附近的经验集中概率。
We develop a Feasible Generalized Least Squares estimator of the date of a structural break in level and/or trend. The estimator is based on a consistent estimate of a <i>T</i>-dimensional inverse autocovariance matrix. A cubic polynomial transformation of break date estimates can be approximated by a nonstandard yet nuisance parameter free distribution asymptotically. The new limiting distribution captures the asymmetry and bimodality in finite samples and is applicable for inference with a single, known, set of critical values. We consider the confidence intervals/sets for break dates based on both Wald-type tests and by inverting multiple likelihood ratio (LR) tests. A simulation study shows that the proposed estimator increases the empirical concentration probability in a small neighborhood of the true break date and potentially reduces the mean squared errors. The LR-based confidence intervals/sets have good coverage while maintaining informative length even with highly persistent errors and small break sizes.