TEMPORAL AGGREGATION AND THE FINITE SAMPLE PERFORMANCE OF SPECTRAL REGRESSION ESTIMATORS IN COINTEGRATED SYSTEMS
通过模拟实验研究了时间加总协整系统中谱回归估计量的有限样本表现，发现带宽和核函数的选择对估计精度至关重要，且仅使用流量数据比使用存量数据或混合数据更有效。
The finite sample performance of spectral regression estimators in temporally aggregated cointegrated systems is investigated via the use of simulation experiments. The simulations address issues such as “optimal” choice of bandwidth parameter and effects of smoothing kernel in constructing estimates of spectral densities that are used by the spectral regression estimators; the effects of stock and flow variables and mixtures of the two, including the relative finite sample efficiency of the estimators under different combinations of stock and flow variables; and the effects of conducting iterations of the spectral estimators. A striking feature of the results is the crucial role that correct choice of bandwidth and kernel function plays in producing accurate estimates of the unknown parameters. Furthermore, estimates obtained using flow data alone are found to be more efficient, in the sense of having smaller variance, than those obtained using stock data alone or mixtures of stocks and flows, thereby confirming in finite samples their relative asymptotic properties.