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Are any growth theories linear? Why we should care about what the evidence tells us

  • Henderson, Daniel J.
  • Papageorgiou, Chris
  • Parmeter, Christopher F.

Recent research on macroeconomic growth has been focused on resolving several key issues, two of which, specification uncertainty of the growth process and variable uncertainty, have received much attention in the recent literature. The standard procedure has been to assume a linear growth process and then to proceed with investigating the relevant variables that determine growth across countries. However, a more appropriate approach would be to recognize that a misspecified model may lead one to conclude that a variable is relevant when in fact it is not. This paper takes a step in this direction by considering conditional variable uncertainty with full blown specification uncertainty. We use recently developed nonparametric model selection techniques to deal with nonlinearities and competing growth theories. We show how one can interpret our results and use them to motivate more intriguing specifications within the traditional studies that use Bayesian Model Averaging or other model selection criteria. We find that the inclusion of nonlinearities is necessary for determining the empirically relevant variables that dictate growth and that nonlinearities are especially important in uncovering key mechanism of the growth process.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 8767.

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Date of creation: 13 May 2008
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Handle: RePEc:pra:mprapa:8767
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  11. Winford H. Masanjala & Chris Papageorgiou, 2008. "Rough and lonely road to prosperity: a reexamination of the sources of growth in Africa using Bayesian model averaging," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 671-682.
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  16. Ley, Eduardo & Steel, Mark F. J., 2006. "Jointness in Bayesian variable selection with applications to growth regression," Policy Research Working Paper Series 4063, The World Bank.
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