Time Series Analysis in Pooled Cross-Sections
提出用谱方法合并多个截面时间序列数据，通过加权方案得到估计量，模拟和实证表明在T=25、N=5的小样本下也有良好性质。
This article proposes the use of spectral methods to pool cross-sectional replications ( N ) of time series data ( T ) for time series analysis. Spectral representations readily suggest a weighting scheme to pool the data. The asymptotically desirable properties of the resulting estimators seem to translate satisfactorily into samples as small as T = 25 with N = 5. Simulation results, Monte Carlo results, and an empirical example help confirm this finding. The article concludes that there are many empirical situations where spectral methods canbe used where they were previously eschewed.