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Parameter Heterogeneity In The Neoclassical Growth Model: A Quantile Regression Approach

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  • Giorgio Canarella

    () (Department of Economics and Statistics, California State University)

  • Stephen Pollard

    (Department of Economics and Statistics, California State University)

Abstract

In this study we examine the issue of parameter heterogeneity in the neoclassical growth model using a quantile regression estimator. Using cross-sectional data on 86 countries covering the period from 1960 to 2000, we estimate a version of the growth model of Mankiw, Romer and Weil (1992). We first estimate the model by OLS. We find that the model is quite successful in explaining the growth empirics of the ¡°average¡± country. We next estimate the model using quantile regression. The results of quantile regression are at odds with the OLS results. We find evidence of partial parameter heterogeneity. Countries whose growth rates are in the higher quantiles respond differently to investment in human and physical capital than do countries whose growth rates are in the lower quantiles. The neoclassical model predicts conditional convergence. The results from the quantile regression do not fully confirm this prediction. We find that convergence is not a generalized phenomenon across the conditional growth distribution, and, in particular, is not characteristic of countries in the lower quantiles. This suggests that an endogenous growth model, where government policies play a more decisive role in shaping the growth process, may be more suitable to describe growth in the lower tail of the distribution, whereas growth in the middle and higher quantiles is better described by the neoclassical model.

Suggested Citation

  • Giorgio Canarella & Stephen Pollard, 2004. "Parameter Heterogeneity In The Neoclassical Growth Model: A Quantile Regression Approach," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 29(1), pages 1-31, June.
  • Handle: RePEc:jed:journl:v:29:y:2004:i:1:p:1-31
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    References listed on IDEAS

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    1. Bernard, Andrew B. & Durlauf, Steven N., 1996. "Interpreting tests of the convergence hypothesis," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 161-173.
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    3. Desdoigts, Alain, 1999. "Patterns of Economic Development and the Formation of Clubs," Journal of Economic Growth, Springer, vol. 4(3), pages 305-330, September.
    4. Caselli, Francesco & Esquivel, Gerardo & Lefort, Fernando, 1996. "Reopening the Convergence Debate: A New Look at Cross-Country Growth Empirics," Journal of Economic Growth, Springer, vol. 1(3), pages 363-389, September.
    5. Buchinsky, Moshe, 1995. "Estimating the asymptotic covariance matrix for quantile regression models a Monte Carlo study," Journal of Econometrics, Elsevier, vol. 68(2), pages 303-338, August.
    6. Koenker, Roger, 2000. "Galton, Edgeworth, Frisch, and prospects for quantile regression in econometrics," Journal of Econometrics, Elsevier, vol. 95(2), pages 347-374, April.
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    Citations

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    Cited by:

    1. LI, Tao & SUN, Laixiang & ZOU, Liang, 2009. "State ownership and corporate performance: A quantile regression analysis of Chinese listed companies," China Economic Review, Elsevier, vol. 20(4), pages 703-716, December.
    2. Hineline, David R., 2008. "Parameter heterogeneity in growth regressions," Economics Letters, Elsevier, vol. 101(2), pages 126-129, November.
    3. Lijuan Huo & Tae-Hwan Kim & Yunmi Kim, 2015. "Revisiting growth empirics based on IV panel quantile regression," Applied Economics, Taylor & Francis Journals, vol. 47(36), pages 3859-3873, August.
    4. 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.
    5. Philip Kostov & Julie Le Gallo, 2015. "Convergence: A Story of Quantiles and Spillovers," Kyklos, Wiley Blackwell, vol. 68(4), pages 552-576, November.
    6. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    7. Andini, Monica & Andini, Corrado, 2014. "Finance, growth and quantile parameter heterogeneity," Journal of Macroeconomics, Elsevier, vol. 40(C), pages 308-322.
    8. Jesus Crespo-Cuaresma & Neil Foster-McGregor & Robert Stehrer, 2009. "The Determinants of Regional Economic Growth by Quantile," wiiw Working Papers 54, The Vienna Institute for International Economic Studies, wiiw.
    9. Salahodjaev, Raufhon & Azam, Sardor, 2015. "Intelligence and gender (in)equality: empirical evidence from developing countries," MPRA Paper 66295, University Library of Munich, Germany.

    More about this item

    Keywords

    Growth Empirics; Quantile Regression; Design Matrix Bootstrap; Neoclassical Growth;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models

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