Testing Error Distribution by Kernelized Stein Discrepancy in Multivariate Time Series Models
基于核化Stein差异的多变量时间序列误差分布检验
Knowing the error distribution is important in many multivariate time series applications. To alleviate the risk of error distribution mis-specification, testing methodologies are needed to detect whether the chosen error distribution is correct. However, the majority of existing tests only deal with the multivariate normal distribution for some special multivariate time series models, and thus cannot be used for testing the often observed heavy-tailed and skewed error…