The maximally selected likelihood ratio test in random coefficient models
研究了随机系数自回归模型中基于最大化选择似然比统计量的变点检验，该检验在样本两端附近也有检测能力，且不依赖数据平稳性，模拟显示功效良好。
Summary In a recent contribution, we developed a family of cumulative sum-based change-point tests in the context of a random coefficient autoregressive model of order 1. In the current paper, we complement the results in that contribution by studying the (maximally selected) likelihood ratio statistic. We show that this has power versus breaks occurring even as close as periods from the beginning/end of sample; moreover, the use of quasi-maximum likelihood-based estimates yields better power properties, with the added bonus of being nuisance-free. Our test statistic has the same distribution—of the Darling–Erdős type—irrespective of whether the data are stationary or not, and can therefore be applied with no prior knowledge of this. Our simulations show that our test has very good power and, when applying a suitable correction to the asymptotic critical values, the correct size. We illustrate the usefulness and generality of our approach through applications to economic and epidemiological time series.