THE ASYMPTOTIC EFFICIENCY OF COINTEGRATION ESTIMATORS UNDER TEMPORAL AGGREGATION
研究了时间聚合对协整系统估计量渐近方差的影响，发现基于流量数据的估计量比存量或混合数据更有效，并提出了改进存量变量效率的方法，对长期货币需求回归有实证意义。
This paper examines the effects of temporal aggregation on the asymptotic variances of estimators in cointegrated systems. Two important findings are obtained. First, estimators based on flow data alone are more efficient than when the data are all stocks or a mixture of stocks and flows. Second, estimators based on flow data are as efficient as when the data are recorded continuously. A method of improving efficiency with stock variables is also proposed, and an empirical illustration of the method is provided in the context of long-run money demand regressions.I thank Roy Bailey, Rex Bergstrom, Roderick McCrorie, a co-editor, and two anonymous referees for helpful comments. I also thank Katsumi Shimotsu for help with some data issues. None of these individuals are implicated, however, in any possible shortcomings of this paper. The financial support provided by the ESRC under grant R000221818 is gratefully acknowledged.