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Estimates Of Technology And Convergence: Simulation Results

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  • Graeme Wells
  • Thanasis Stengos

Abstract

Using a Solow-Swan model with a stochastic saving rate and stochastic productivity we analyse the distributions of parameter estimates that emerge under various choices of technology, and of the dimension of the panel on which cross-section regressions are based. There are distinct asymmetries that characterize these distributions. These asymmetries become more pronounced when the effects of a near-unit root in the productivity shock become magnified over a longer time horizon and when the underlying production function is not Cobb-Douglas. Consequently, relying on traditional econometric transformations of these parameter estimates based on symmetric distributions, such as t-ratios, will be quite misleading if one tries to assess technology parameters and B-convergence.

Suggested Citation

  • Graeme Wells & Thanasis Stengos, 2006. "Estimates Of Technology And Convergence: Simulation Results," CAMA Working Papers 2006-07, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  • Handle: RePEc:een:camaaa:2006-07
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    File URL: https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2017-02/7_wells_stengos_2006.pdf
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    References listed on IDEAS

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    1. Bianchi, Marco, 1997. "Testing for Convergence: Evidence from Non-parametric Multimodality Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(4), pages 393-409, July-Aug..
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    3. Robert C. Merton, 1975. "An Asymptotic Theory of Growth Under Uncertainty," Review of Economic Studies, Oxford University Press, vol. 42(3), pages 375-393.
    4. Buchinsky, Moshe, 1994. "Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression," Econometrica, Econometric Society, vol. 62(2), pages 405-458, March.
    5. Mirman, Leonard J., 1973. "The steady state behavior of a class of one sector growth models with uncertain technology," Journal of Economic Theory, Elsevier, vol. 6(3), pages 219-242, June.
    6. Binder, Michael & Pesaran, M Hashem, 1999. "Stochastic Growth Models and Their Econometric Implications," Journal of Economic Growth, Springer, vol. 4(2), pages 139-183, June.
    7. Quah, Danny T., 1996. "Empirics for economic growth and convergence," European Economic Review, Elsevier, vol. 40(6), pages 1353-1375, June.
    8. Cellini, Roberto, 1997. "Implications of Solow's Growth Model in the Presence of a Stochastic Steady State," Journal of Macroeconomics, Elsevier, vol. 19(1), pages 135-153, January.
    9. Mirman, Leonard J, 1972. "On the Existence of Steady State Measures for One Sector Growth Models with Uncertain Technology," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 13(2), pages 271-286, June.
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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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