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经济变量时间序列中缺失观测的预测AI 解读

A least squares predictor is derived to interpolate missing observations of an economic time series in which the recording interval has been shortened. This predictor uses all the available information, but depends on the unknown autocovariance structure of the series. A much simpler predictor, not involving the autocovariances, is shown to be almost optimal whenever the process has the typical economic spectral shape, and simulation experiments indicate that it is superior…

经济变量时间序列缺失观测的预测AI 解读

A least squares predictor is derived to interpolate missing observations of an economic time series in which the recording interval has been shortened. This predictor uses all the available information, but depends on the unknown autocovariance structure of the series. A much simpler predictor, not involving the autocovariances, is shown to be almost optimal whenever the process has the typical economic spectral shape, and simulation experiments indicate that it is superior…