Resampling-Based Variance Estimation for Labour Force Surveys.
Angelo J. Canty and A. C. Davison
Abstract
Labour force surveys are conducted to estimate
quantities such as the unemployment rate and the number of people in work.
Interest is typically both in estimates at a
given time and in changes between two successive time-points. Calibration
of the sample to force agreement with known population margins results in
random weights being assigned to each response, but the usual
methods of variance estimation do not account for this.
This paper describes how resampling methods - the jackknife,
jackknife linearization, balanced repeated replication, and the bootstrap
- can be used to do so. {We also discuss} implementation issues, and
compare the methods by simulation based on data from the UK Labour
Force Survey. The broad conclusions are these: bootstrap and jackknife
linearization are less computer-intensive than the other resampling methods
for such applications and give better standard errors; `standard'
methods can be badly biased downwards; and it is essential to take
variability of the weights into account.
Keywords:
Balanced repeated replication; Bootstrap; Calibration;
Jackknife; Jackknife linearization;
Panel survey; Post-stratification; Raking ratio.
This article was published in The Statistician (1999),
48 379-391.
This page is maintained by Angelo Canty,
cantya@mcmaster.ca
Last updated on July 23, 2001