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Bayesian comparison of production function-based and time-series GDP models

Author

Listed:
  • Jacek Osiewalski

    (Cracow University of Economics)

  • Justyna Wróblewska

    (Cracow University of Economics)

  • Kamil Makieła

    (Cracow University of Economics)

Abstract

A purely Bayesian vector autoregression (VAR) framework is proposed to formulate and compare tri-variate models for the logs of the economy-wide aggregates of output and inputs (physical capital and labour). The framework is derived based on the theory of the aggregate production function, but at the same time, accounts for the dynamic properties of macroeconomic data, which makes it particularly appealing for modelling GDP. Next, using the proposed framework we confront a-theoretical time-series models with those that are based on aggregate production function-type relations. The common knowledge about capital and labour elasticities of output as well as on their sum is used in order to formulate prior distribution for each tri-variate model, favouring the linearly homogenous Cobb–Douglas production function-type relation. In spite of this, production function-based co-integration models fail empirical comparisons with simple VAR structures, which describe the three aggregates by three stochastic trends.

Suggested Citation

  • Jacek Osiewalski & Justyna Wróblewska & Kamil Makieła, 2020. "Bayesian comparison of production function-based and time-series GDP models," Empirical Economics, Springer, vol. 58(3), pages 1355-1380, March.
    • Handle: RePEc:spr:empeco:v:58:y:2020:i:3:d:10.1007_s00181-018-1575-8
    DOI: 10.1007/s00181-018-1575-8
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    1. van den Broeck, Julien & Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1994. "Stochastic frontier models : A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 61(2), pages 273-303, April.
    2. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    3. Fisher, Franklin M, 1969. "The Existence of Aggregate Production Functions," Econometrica, Econometric Society, vol. 37(4), pages 553-577, October.
    4. Koop, Gary & Osiewalski, Jacek & Steel, Mark F J, 1999. "The Components of Output Growth: A Stochastic Frontier Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(4), pages 455-487, November.
    5. Jacek Osiewalski & Mark Steel, 1998. "Numerical Tools for the Bayesian Analysis of Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 10(1), pages 103-117, July.
    6. Koop, Gary & Ley, Eduardo & Osiewalski, Jacek & Steel, Mark F. J., 1997. "Bayesian analysis of long memory and persistence using ARFIMA models," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 149-169.
    7. Chikuse, Yasuko, 1990. "The matrix angular central Gaussian distribution," Journal of Multivariate Analysis, Elsevier, vol. 33(2), pages 265-274, May.
    8. J.‐P. Florens & M. Mouchart, 1985. "Conditioning In Dynamic Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 6(1), pages 15-34, January.
    9. Charles I. Jones, 2005. "The Shape of Production Functions and the Direction of Technical Change," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(2), pages 517-549.
    10. Koop, Gary & Osiewalski, Jacek & Steel, Mark F J, 2000. "Modeling the Sources of Output Growth in a Panel of Countries," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 284-299, July.
    11. Growiec, Jakub, 2013. "A microfoundation for normalized CES production functions with factor-augmenting technical change," Journal of Economic Dynamics and Control, Elsevier, vol. 37(11), pages 2336-2350.
    12. Kamil Makieła, 2014. "Bayesian Stochastic Frontier Analysis of Economic Growth and Productivity Change in the EU, USA, Japan and Switzerland," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 6(3), pages 193-216, September.
    13. Jakub Growiec, 2008. "A new class of production functions and an argument against purely labor‐augmenting technical change," International Journal of Economic Theory, The International Society for Economic Theory, vol. 4(4), pages 483-502, December.
    14. Gary Koop & Roberto León-González & Rodney W. Strachan, 2010. "Efficient Posterior Simulation for Cointegrated Models with Priors on the Cointegration Space," Econometric Reviews, Taylor & Francis Journals, vol. 29(2), pages 224-242, April.
    15. Florens, J.-P. & Mouchart, M., 1985. "Conditioning in dynamic models," LIDAM Reprints CORE 624, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    16. Gary Koop & Jacek Osiewalski & Mark F. J. Steel, 1999. "The Components of Output Growth: A Stochastic Frontier Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(4), pages 455-487, November.
    17. Justyna Wróblewska, 2009. "Bayesian Model Selection in the Analysis of Cointegration," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 1(1), pages 57-69, March.
    18. Shaikh, Anwar, 1974. "Laws of Production and Laws of Algebra: The Humbug Production Function," The Review of Economics and Statistics, MIT Press, vol. 56(1), pages 115-120, February.
    19. Jesus Felipe & John S.L. McCombie, 2013. "The Aggregate Production Function and the Measurement of Technical Change," Books, Edward Elgar Publishing, number 1975.
    20. Ali Alichi & Olivier Bizimana & Mr. Douglas Laxton & Kadir Tanyeri & Hou Wang & Jiaxiong Yao & Fan Zhang, 2017. "Multivariate Filter Estimation of Potential Output for the United States," IMF Working Papers 2017/106, International Monetary Fund.
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    Cited by:

    1. Kamil Makieła & Błażej Mazur, 2022. "Model uncertainty and efficiency measurement in stochastic frontier analysis with generalized errors," Journal of Productivity Analysis, Springer, vol. 58(1), pages 35-54, August.
    2. Kamil Makieła & Błażej Mazur & Jakub Głowacki, 2022. "The Impact of Renewable Energy Supply on Economic Growth and Productivity," Energies, MDPI, vol. 15(13), pages 1-13, June.
    3. Jakub Boratyński & Jacek Osiewalski, 2021. "Bayesian Estimation of Capital Stock and Depreciation in the Production Function Framework," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 13(4), pages 455-486, December.

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    More about this item

    Keywords

    Bayesian inference; VAR models; Economic growth models; Co-integration analysis; Aggregate production function; Potential output;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General

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