The variances of non-parametric estimates of the cross-sectional distribution of durations

References

• Aalen, O. O. (1978). Nonparametric inference for a family of counting processes. Annals of Statistics 6:701–726.
   Web of Science ®Google Scholar
• Andersen, P. K., Borgan, Ø., Gill, R. D., Keiding, N. (1993). Statistical Models Based on Counting Processes, New York, NY: Springer.
   Google Scholar
• Baharad, E., Eden, B. (2004). Price rigidity and price dispersion: Evidence from micro data. Economic Modelling 7:613–641.
   Google Scholar
• Bohoris, G. A. (1994). Comparison of the cumulative-hazard and Kaplan–Meier estimators of the survivor function. IEEE Transactions on Reliability 43(2):230–232. doi:10.1109/24.294997
   Web of Science ®Google Scholar
• Breslow, N., Crowley, J. (1974). A large sample study of the life table and product limit estimates under random censorship. Annals of Statistics 2:437–453.
   Web of Science ®Google Scholar
• Coenen, G., Mohr, M., Straub, R. (2008). Fiscal consolidation in the euro area: long-run benefits and short-run costs. Economic Modelling 25(5):912–932. doi:10.1016/j.econmod.2007.11.011
   Web of Science ®Google Scholar
• Colosimo, E., Ferreira, F., Oliveira, M., Sousa, C. (2002). Empirical comparisons between Kaplan-Meier and Nelson-Aalen survival function estimators. Journal of Statistical Computation and Simulation 72(4):299–308. doi:10.1080/00949650212847
   Web of Science ®Google Scholar
• Cox, C. (1990). Fieller’s theorem, the likelihood and the Delta method. Oxford Bulletin of Economics 46(3):709–718.
   Google Scholar
• Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological) 34(2):187–220. doi:10.1111/j.2517-6161.1972.tb00899.x
   Web of Science ®Google Scholar
• 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
• 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
   Web of Science ®Google Scholar
• Dixon, H., Luintel, K., Tian, K. (2020). The impact of the 2008 crisis on UK prices: what we can learn from the CPI microdata. Oxford Bulletin of Economics and Statistics 82(6):1322–1341. doi:10.1111/obes.12373
   Web of Science ®Google Scholar
• Dixon, H., Siciliani, L. (2009). Waiting-time targets in the healthcare sector: how long are we waiting? Journal of Health Economics 28(6):1081–1098. doi:10.1016/j.jhealeco.2009.09.003
   PubMed Web of Science ®Google Scholar
• Dixon, H., Tian, K. (2017). What we can learn about the behavior of firms from the average monthly frequency of price-changes: an application to the UK CPI data. Oxford Bulletin of Economics and Statistics 79(6):907–932. doi:10.1111/obes.12173
   Web of Science ®Google Scholar
• Efron, B. (1981). Censored data and the bootstrap. Journal of the American Statistical Association 76(374):312–319. doi:10.1080/01621459.1981.10477650
   Web of Science ®Google Scholar
• Fleming, T. R., Harrington, D. P. (1991). Counting Process and Survival Analysis, New York: John Wiley and Sons.
   Google Scholar
• Franz, V. (2007). Ratios: A short guide to confidence limits and proper use. Working Paper. ArXiv Preprint arXiv:0710.2024
   Google Scholar
• Gillesple, M. J., Fisher, L. (1979). Confidence bands for the Kaplan-Meier survival curve estimate. Annals of Statistics 7:920–924.
   Web of Science ®Google Scholar
• Greenwood, M. (1926). The natural duration of cancer. Reports on Public Health and Medical Subjects 33:1–26.
   Google Scholar
• Kalbfleisch, J. D., Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data, New York: John Wiley and Sons, 2nd edition.
   Google Scholar
• Kaplan, E. L., Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 53(282):457–481. doi:10.1080/01621459.1958.10501452
   Web of Science ®Google Scholar
• Kiviet, J. F., Phillips, G. D. A. (2014). Improved variance estimation of maximum likelihood estimators in stable first-order dynamic regression models. Computational Statistics & Data Analysis 76:424–448. doi:10.1016/j.csda.2013.09.021
   Web of Science ®Google Scholar
• Mantel, N. (1966). Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemotherapy Reports 50(3):163–170.
   PubMedGoogle Scholar
• Nair, V. N. (1981). Plots and tests for goodness of fit with randomly censored data. Biometrika 68(1):99–103. doi:10.1093/biomet/68.1.99
   Web of Science ®Google Scholar
• Nair, V. N. (1984). Confidence bands for survival functions with censored data: a comparative study. Technometrics 26(3):265–275. doi:10.1080/00401706.1984.10487964
   Web of Science ®Google Scholar
• Nelson, W. (1972). Theory and application of hazard plotting for censored failure data. Technometrics 14(4):945–965. doi:10.1080/00401706.1972.10488991
   Web of Science ®Google Scholar
• Siciliani, L., Borowitz, M., Moran, V. (2013). Waiting Time Policies in the Health Sector: What Works? OECD Health Policy Studies, Paris: OECD Publishing.
   Google Scholar
• Taylor, J. B. (1980). Aggregate dynamics and staggered contracts. Journal of Political Economy 88(1):1–23. doi:10.1086/260845
   Web of Science ®Google Scholar
• Taylor, J. B. (2016). The staying power of staggered wage and price setting models in macroeconomics, In: John B. Taylor, Harald Uhlig, eds., Volume 2 of Handbook of Macroeconomics, Amsterdam: Elsevier, pp. 2009–2042.
   Google Scholar
• Tian, M., Dixon, H. (2022). The confidence interval of cross-sectional distribution of durations. Working Paper. Available online at: https://doi.org/10.13140/RG.2.2.12152.52486
   Google Scholar