Conferencistas Nacionais

Covariance Structure Models for Longitudinal Complex Survey Data

Marcel de Toledo Vieira (UFJF)*

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 are assessed in a simulation study, based upon data from the British Household Panel Survey. The point estimators have broadly similar performance, 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 compared to a GLS estimator. Some extensions to model fitting statistics when working with longitudinal data in a complex survey design framework are also considered.

Key words: longitudinal survey; covariance structure; multistage sampling; stratification; weighting; model fitting.

* Joint work with Chris J. Skinner (Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, United Kingdom).