Nonnested model comparisons for time series
研究了非嵌套时间序列模型的比较方法，推导了似然比统计量的中心极限定理，并通过模拟和零售数据验证了方法。
This paper addresses the topic of nonnested time series model comparisons. The main result is a central limit theorem for the likelihood ratio statistic when the models are nonnested and non-equivalent. The concepts of model equivalence and forecast equivalence, which are important for determining the parameter subset corresponding to the null hypothesis, are developed. The method is validated through a simulation study and illustrated on a retail time series.