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

Author

Listed:
  • Henderson, Daniel J.
  • Papageorgiou, Chris
  • Parmeter, Christopher F.
Abstract
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.

Suggested Citation

  • Henderson, Daniel J. & Papageorgiou, Chris & Parmeter, Christopher F., 2008. "Are any growth theories linear? Why we should care about what the evidence tells us," MPRA Paper 8767, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:8767
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    File URL: https://mpra.ub.uni-muenchen.de/8767/1/MPRA_paper_8767.pdf
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    References listed on IDEAS

    as
    1. 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.
    2. Peter Hall & Qi Li & Jeffrey S. Racine, 2007. "Nonparametric Estimation of Regression Functions in the Presence of Irrelevant Regressors," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 784-789, November.
    3. Racine, Jeff & Li, Qi, 2004. "Nonparametric estimation of regression functions with both categorical and continuous data," Journal of Econometrics, Elsevier, vol. 119(1), pages 99-130, March.
    4. Ketteni, Elena & Mamuneas, Theofanis P. & Stengos, Thanasis, 2007. "Nonlinearities in economic growth: A semiparametric approach applied to information technology data," Journal of Macroeconomics, Elsevier, vol. 29(3), pages 555-568, September.
    5. David F. Hendry & Hans-Martin Krolzig, 2004. "We Ran One Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(5), pages 799-810, December.
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    Citations

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    Cited by:

    1. Rodríguez, Francisco & Shelton, Cameron A., 2013. "Cleaning up the kitchen sink: Specification tests and average derivative estimators for growth econometrics," Journal of Macroeconomics, Elsevier, vol. 38(PB), pages 260-273.
    2. Kottaridi, Constantina & Stengos, Thanasis, 2010. "Foreign direct investment, human capital and non-linearities in economic growth," Journal of Macroeconomics, Elsevier, vol. 32(3), pages 858-871, September.
    3. Aurangzeb Zeb & Thanasis Stengos, 2013. "The Role of Foreign Direct Investment (FDI) in a Dualistic Growth Framework: An Application of Smooth Coefficient Semi-parametric Approach," Working Paper series 55_13, Rimini Centre for Economic Analysis.
    4. Libman, Alexander, 2008. "Democracy and growth: is the effect non-linear?," MPRA Paper 17795, University Library of Munich, Germany.

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    More about this item

    Keywords

    Growth Nonlinearities; Irrelevant Variables; Least Squares Cross Validation; Bayesian Model Averaging; Parameter Heterogeneity;
    All these keywords.

    JEL classification:

    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General

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