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A new econometric approach to testing for economic growth convergence is overviewed. The method is applicable to panel data, involves a simple regression based one-sided t-test, and can be used to form a clustering algorithm to assess the existence of growth convergence clubs. The approach allows for heterogeneous technology, utilizes some new asymptotic theory for nonlinear dynamic factor models, and is easy to implement. Some background growth theory is given which shows the form of augmented Solow regression (ASR) equations in the presence of heterogeneous technology and explains sources of potential misspecification that can arise in conventional formulations of ASR equations that are used to analyze growth convergence and growth determinants. A short empirical application is given illustrating some aspects of the methodology involving technological heterogeneity and learning in growth …