Asymmetric Time Series and Temporal Aggregation
研究采样频率对非线性检测的影响，发现月度非对称序列在聚合为季度或年度后可能变为对称，并用瑞典失业数据验证了年度序列的对称性。
The detection of nonlinearities could depend on the sampling frequency. Asymmetric monthly series may become symmetric when aggregated to quarterly or annual frequencies. We test against nonlinearity using the nonlinear autoregressive asymmetric moving average (ARasMA) model, which nests the linear ARMA model as a special case. Using monthly, quarterly, and annual Swedish unemployment series, we find support for symmetry/linearity in the annual series but not in the monthly and quarterly series. © 1999 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology