Disaggregation of Time Series Models
提出一种从给定的聚合模型推导出分解模型的方法，用于数据分解，适用于非季节性和季节性模型，并通过实证例子说明。
SUMMARY We develop a model disaggregation method to derive a disaggregate model from a given aggregate model, which is then used to perform data disaggregation. Since a time series model and its autocovariance structure are closely related, we approach the problem by exploring the possibility of estimating the autocovariance structure for the unobserved disaggregated series from the available autocovariances of an aggregate model. Let the time series aggregates be the non-overlapping sums of m consecutive disaggregated observations. Given an aggregate autoregressive integrated moving average ARIMA(p, d, r) model with r ≤ p + d + 1, assume that there is no hidden periodicity of order m. It is shown that, if m is odd or if m is even but all the real roots of the autoregressive polynomial of the given aggregate model are positive, then there exists a disaggregate model whose autocovariances can be uniquely derived from the autocovariances of the given aggregate model. Both non-seasonal and seasonal models are discussed. Empirical examples are presented to illustrate the method.