Resampling time series by missing values techniques

Andrés Modesto Alonso Fernández, Daniel Peña and Juan Romo

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: For strongly dependent data, deleting blocks of observations is expected to produce bias as in the moving block jackknife of KOnsch (1989) and Liu and Singh (1992). We propose an alternative technique which considers the blocks of deleted observations in the blockwise jackknife as missing data which are replaced by missing values estimates incorporating the observations dependence structure. Thus, the variance estimator is a weighted sample variance of the statistic evaluated in a "complete" series. We establish consistency for the variance and distribution of the sample mean. Also we extent this missing values approach to the blockwise bootstrap by assuming some missing observations among two consecutive blocks. We present the results of an extensive Monte Carlo study to evaluate the performance of the proposed methods in finite sample sizes in which it is shown that our proposal produces estimates of the variance of several time series statistics with smaller mean squared error than previous procedures.

Keywords: Jackknife; Bootstrap; Missing; values; Time; series (search for similar items in EconPapers)
Date: 2000-07
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://e-archivo.uc3m.es/rest/api/core/bitstreams ... 0e037d0b252c/content (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:9923

Access Statistics for this paper

More papers in DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística
Bibliographic data for series maintained by Ana Poveda ().