Summary Phillips and Sul (Journal of Applied Econometrics 2009, 24, 1153–1185) provide an algorithm to identify convergence clubs in a dynamic factor model of economic transition and growth. We provide a narrow replication of their key results, using the open source R software instead of the original GAUSS routines. We are able to exactly replicate their results on convergence clubs, corresponding point estimates and standard errors. We comment on minor differences between their reported results and their clustering algorithm. We propose simple adjustments of the original algorithm to make manual intervention unnecessary. The adjustments allow automated application of the algorithm to other data. Copyright © 2016 John Wiley & Sons, Ltd.

Recently, using data on Canadian housing, Parmeter, Henderson, and Kumbhakar (2007) found that a nonparametric approach for estimating hedonic prices is superior to formerly suggested parametric and semiparametric specifications. We carefully analyze this data set by applying a nonparametric specification test and simulation based forecast comparisons. For the case at issue our results suggest that a previously proposed parametric specification cannot be rejected.

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The paper proposes a cross-validation method to address the question of specification search in a multiple nonlinear quantile regression framework. Linear parametric, spline-based partially linear and kernelbased fully nonparametric specifications are contrasted as competitors using cross-validated weighted L 1 -norm based goodness-of-fit and prediction error criteria. The aim is to provide a fair comparison with respect to estimation accuracy and/or predictive ability for different semi-and nonparametric specification paradigms. This is challenging as the model dimension cannot be estimated for all competitors and the meta-parameters such as kernel bandwidths, spline knot numbers and polynomial degrees are difficult to compare. General issues of specification comparability and automated data-driven meta-parameter selection are discussed. The proposed method further allows us to assess the balance between fit and model complexity. An extensive Monte Carlo study and an application to a well-known data set provide empirical illustration of the method.