Pooling‐Based Data Interpolation and Backdating
研究了将不同方法得到的插值或回溯时间序列进行池化，能否提升数据质量。模拟和宏观经济数据实证表明池化有积极作用。
Abstract. Pooling forecasts obtained from different procedures typically reduces the mean square forecast error and more generally improve the quality of the forecast. In this paper, we evaluate whether pooling‐interpolated or‐backdated time series obtained from different procedures can also improve the quality of the generated data. Both simulation results and empirical analyses with macroeconomic time series indicate that pooling plays a positive and important role in this context also.