The variances of non-parametric estimates of the cross-sectional distribution of durations
推导了三种持续时间分布的非参数估计量的方差公式，并通过蒙特卡洛方法评估其表现，最后应用于英国CPI微观价格数据和医院等待时间数据，发现估计量均显著。
This paper focuses on the link between non-parametric survival analysis and three distributions. The delta method is applied to derive the variances of the non-parametric estimators of three distributions: the distribution of durations (DD), the cross-sectional distribution of ages (CSA) and the cross-sectional distribution of (completed) durations (CSD). The non-parametric estimator of the the cross-sectional distribution of durations (CSD) has been defined and derived by Dixon (2012 Dixon, H. (2012). A unified framework for using micro-data to compare dynamic time-dependent price-setting models. B.E. Journal of Macroeconomics (Contributions) 12:1–43.[Web of Science ®] , [Google Scholar]) and used in the generalized Taylor price model (GTE) by Dixon and Le Bihan (2012 Dixon, H., Le Bihan, H. (2012). Generalised Taylor and generalised Calvo price and wage setting: micro-evidence with macro implications. The Economic Journal 122(560):532–554. doi:10.1111/j.1468-0297.2012.02497.x[Crossref], [Web of Science ®] , [Google Scholar]). The Monte Carlo method is applied to evaluate the variances of the estimators of DD and CSD and how their performance varies with sample size and the censoring of data. We apply those estimators to two data sets: the UK CPI micro-price data and waiting-time data from UK hospitals. Both the estimates of the distributions and their variances are calculated. Depending on the empirical results, the estimated variances indicate that the DD and CSD estimators are all significant.