Aust. N. Z. J. Statist. 42(2), 2000, 159–177 A GENERALIZED ESTIMATING EQUATIONS APPROACH FOR ANALYSIS OF THE IMPACT OF NEW TECHNOLOGY ON A TRAWL FISHERY JANET BISHOP ∗1 ,DAVID DIE 1 AND YOU-GAN WANG 2,3 CSIRO Marine Research and CSIRO Mathematical & Information Sciences Summary The article describes a generalized estimating equations approach that was used to inves- tigate the impact of technology on vessel performance in a trawl fishery during 1988–96, while accounting for spatial and temporal correlations in the catch–effort data. Robust estimation of parameters in the presence of several levels of clustering depended more on the choice of cluster definition than on the choice of correlation structure within the cluster. Models with smaller cluster sizes produced stable results, while models with larger cluster sizes, that may have had complex within-cluster correlation structures and that had within- cluster covariates, produced estimates sensitive to the correlation structure. The preferred model arising from this dataset assumed that catches from a vessel were correlated in the same years and the same areas, but independent in different years and areas. The model that assumed catches from a vessel were correlated in all years and areas, equivalent to a random effects term for vessel, produced spurious results. This was an unexpected finding that highlighted the need to adopt a systematic strategy for modelling. The article proposes a modelling strategy of selecting the best cluster definition first, and the working correlation structure (within clusters) second. The article discusses the selection and interpretation of the model in the light of background knowledge of the data and utility of the model, and the potential for this modelling approach to apply in similar statistical situations. Key words: covariance; fishing power; generalized estimating equations; overdispersion; Poisson; spatial and temporal correlations. 1. Introduction In this paper, we show some practical complications in using the generalized estimating equations (GEE) approach to analyse data with spatial and temporal correlations, and suggest a strategy for resolving some of these problems, illustrating it by analysis of data from a fisheries application. The aim of the analysis is to estimate the extent to which new technology has increased the catching power of a fishing fleet. This topic is important for fishery managers, who generally share a common objective: to ensure the long-term viability of the fishery. To achieve this objective, many fisheries are managed by so-called ‘input controls’ such as restrictions on Received November 1998; revised August 1999; accepted November 1999. ∗ Author to whom correspondence should be addressed. 1 CSIRO Division of Marine Research, PO Box 120, Cleveland, QLD 4163, Australia. e-mail: janet.bishop@marine.csiro.au 2 CSIRO Mathematical & Information Sciences, PO Box 120, Cleveland, QLD 4163, Australia. 3 Dept Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA. Acknowledgments. The authors thank the fishers of the Northern Prawn Fishery who provided their catch records, and Carolyn M. Robins (Bureau of Rural Sciences, Canberra, Australia) and Margot Sachse (Australian Fisheries Management Authority, Canberra) for establishing a validated dataset. The authors are grateful for comments on earlier drafts from Richard Morton (CSIRO CMIS, Canberra) and Andre Punt (CSIRO Marine Research, Hobart) and three anonymous reviewers, that influenced the direction of the final version. This project was supported by the Australian Fish Management Authority Research Fund. c  Australian Statistical Publishing Association Inc. 2000. Published by Blackwell Publishers Ltd, 108 Cowley Road, Oxford OX4 1JF, UK and 350 Main Street, Malden MA 02148, USA