Dealing with heterogeneity, nonlinearity and club misclassification in growth convergence: A nonparametric two-step approach
Classical growth convergence regressions fail to account for various sources of heterogeneity and nonlinearity. While recent contributions are able to address either the one or the other, we present a simple two-step method to address both issues. Based on a slightly augmented version of a recently proposed algorithm to identify convergence clubs, we formulate a flexible nonlinear framework which allows to analyze convergence effects on both individual and club level, while alleviating potential misclassification in the club formation process using simultaneous smoothing over the club structure. The merits of the method are illustrated for data on different aggregational levels.
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