Estimating models for panel survey data under complex sampling
Estimating models for panel survey data under complex sampling
Complex designs are often used to select the sample which is followed over time in a panel survey. We consider some parametric models for panel data and discuss methods of estimating the model parameters which allow for complex schemes. We incorporate survey weights into alternative point estimation procedures. These procedures include pseudo maximum likelihood (PML) and various forms of generalized least squares (GLS). We also consider variance estimation using linearization methods to allow for complex sampling. The behaviour of the proposed inference procedures is assessed in a simulation study, based upon data from the British Household Panel Survey. The point estimators have broadly similar performances, with few significant gains from GLS estimation over PML estimation. The need to allow for clustering in variance estimation methods is demonstrated. Linearization variance estimation performs better, in terms of bias, for the PML estimator than for a GLS estimator.
longitudinal survey, covariance structure, multistage sampling, stratification, weighting
343-364
Vieira, Marcel D.T.
3318b688-b2f8-48d4-b92a-6fb65a045514
Skinner, Chris J.
dec5ef40-49ef-492a-8a1d-eb8c6315b8ce
September 2008
Vieira, Marcel D.T.
3318b688-b2f8-48d4-b92a-6fb65a045514
Skinner, Chris J.
dec5ef40-49ef-492a-8a1d-eb8c6315b8ce
Vieira, Marcel D.T. and Skinner, Chris J.
(2008)
Estimating models for panel survey data under complex sampling.
Journal of Official Statistics, 24 (3), .
Abstract
Complex designs are often used to select the sample which is followed over time in a panel survey. We consider some parametric models for panel data and discuss methods of estimating the model parameters which allow for complex schemes. We incorporate survey weights into alternative point estimation procedures. These procedures include pseudo maximum likelihood (PML) and various forms of generalized least squares (GLS). We also consider variance estimation using linearization methods to allow for complex sampling. The behaviour of the proposed inference procedures is assessed in a simulation study, based upon data from the British Household Panel Survey. The point estimators have broadly similar performances, with few significant gains from GLS estimation over PML estimation. The need to allow for clustering in variance estimation methods is demonstrated. Linearization variance estimation performs better, in terms of bias, for the PML estimator than for a GLS estimator.
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Published date: September 2008
Keywords:
longitudinal survey, covariance structure, multistage sampling, stratification, weighting
Identifiers
Local EPrints ID: 64432
URI: http://eprints.soton.ac.uk/id/eprint/64432
ISSN: 0282-423X
PURE UUID: d985b2a8-ce5a-411b-8faa-b00ef37d9fe7
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Date deposited: 12 Jan 2009
Last modified: 11 Dec 2021 18:22
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Contributors
Author:
Marcel D.T. Vieira
Author:
Chris J. Skinner
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