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Modeling technology and technological change in manufacturing: how do countries differ?

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Listed author(s):
  • Eberhardt, Markus
  • Teal, Francis

Abstract

In this paper we ask how technological differences in manufacturing across countries can best be modeled when using a standard production function approach. We show that it is important to allow for differences in technology as measured by differences in parameters. Of similar importance are time-series properties of the data and the role of dynamic processes, which can be thought of as aspects of technological change. Regarding the latter we identify both an element that is common across all countries and a part which is country-specific. The estimator we develop, which we term the Augmented Mean Group estimator (AMG), is closely related to the Mean Group version of the Pesaran (2006) Common Correlated Effects estimator. Once we allow for parameter heterogeneity and the underlying time-series properties of the data we are able to show that the parameter estimates from the production function are consistent with information on factor shares.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 10690.

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Date of creation: Apr 2008
Handle: RePEc:pra:mprapa:10690
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Keywords: Manufacturing Production; Parameter Heterogeneity; Nonstationary Panel Econometrics; Cross-section Dependence;

Other versions of this item:

Find related papers by JEL classification:
  • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
  • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
  • O14 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Industrialization; Manufacturing and Service Industries; Choice of Technology

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Cited by:

  1. Ayako Saiki, 2015. "The Endogeneity of Exchange Rate Pass-Through: Some European Evidence," Open Economies Review, Springer, vol. 26(5), pages 893-909, November.
  2. Markus Eberhardt & Francis Teal, 2011. "Econometrics For Grumblers: A New Look At The Literature On Cross‐Country Growth Empirics," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 109-155, 02.
  3. Eberhardt, Markus & Bond, Stephen, 2009. "Cross-section dependence in nonstationary panel models: a novel estimator," MPRA Paper 17692, University Library of Munich, Germany.
  4. Elliott, Robert J.R. & Sun, Puyang & Zhu, Tong, 2017. "The direct and indirect effect of urbanization on energy intensity: A province-level study for China," Energy, Elsevier, vol. 123(C), pages 677-692.
  5. Markus Eberhardt & Francis Teal, 2009. "A Common Factor Approach to Spatial Heterogeneity in Agricultural Productivity Analysis," CSAE Working Paper Series 2009-05, Centre for the Study of African Economies, University of Oxford.
  6. repec:eee:juipol:v:45:y:2017:i:c:p:45-60 is not listed on IDEAS
  7. Roland-Holst, David & Sugiyarto, Guntur, 2014. "Growth Horizons for a Changing Asian Regional Economy," ADB Economics Working Paper Series 392, Asian Development Bank.
  8. Anna Bottaso & Carolina Castagnetti & Maurizio Conti, 2011. "And Yet they Co-Move! Public Capital and Productivity in OECD: A Panel Cointegration Analysis with Cross-Section Dependence," Quaderni di Dipartimento 154, University of Pavia, Department of Economics and Quantitative Methods.

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