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Assessing parametric misspecification and heterogeneity in growth regression

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  • Harry Haupt
  • Verena Petring

Abstract

A fully nonparametric analysis is applied to address the problems of nonlinearity and heterogeneity in classical growth regression models using original data from seminal contributions in this field. Nonparametric specification tests and in-sample goodness-of-fit measures, as well as cross-validation based out-of-sample measures provide considerable evidence for parametric misspecification and a superior performance of a nonparametric model, despite the small sample size. In contrast to recent contributions identifying heterogeneity as the primal source of misspecification, a formal and graphical analysis does not reveal evidence for heterogeneity in a parametric and nonparametric quantile regression framework.

Suggested Citation

  • Harry Haupt & Verena Petring, 2011. "Assessing parametric misspecification and heterogeneity in growth regression," Applied Economics Letters, Taylor & Francis Journals, vol. 18(4), pages 389-394.
    • Handle: RePEc:taf:apeclt:v:18:y:2011:i:4:p:389-394
    DOI: 10.1080/13504851003670643
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    Cited by:

    1. Haupt, Harry & Meier, Verena, 2016. "Dealing with heterogeneity, nonlinearity and club misclassification in growth convergence: A nonparametric two-step approach," Center for Mathematical Economics Working Papers 455, Center for Mathematical Economics, Bielefeld University.
    2. Haupt, Harry & Schnurbus, Joachim & Semmler, Willi, 2018. "Estimation of grouped, time-varying convergence in economic growth," Econometrics and Statistics, Elsevier, vol. 8(C), pages 141-158.

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