The Dimensionality of the Aliasing Problem in Models with Rational Spectral Densities
重新审视从离散时间数据识别连续时间随机过程参数时的混叠问题，分析限制为有理谱密度矩阵能否减少观测等价模型的数量，对使用有理参数化分析时间序列数据的研究者有用。
This paper reconsiders the aliasing problem of identifying the parameters of a continuous time stochastic process from discrete time data. It analyzes the extent to which restricting attention to processes with rational spectral density matrices reduces the number of observationally equivalent models. It focuses on rational specifications of spectral density matrices since rational parameterizations are commonly employed in the analysis of the time series data.