IDEAS home Printed from https://ideas.repec.org/p/ssb/dispap/342.html
   My bibliography  Save this paper

Parametric Aggregation of Random Coefficient Cobb-Douglas Production Functions: Evidence from Manufacturing Industries

Author

Listed:

Abstract

A panel data study of parametric aggregation of a production function is presented. A four-factor Cobb-Douglas function with random and jointly normal coefficients and jointly log-normal inputs is used. Since, if the number of micro units is not too small and certain regularity conditions are met, aggregates expressed as arithmetic means can be associated with expectations, we consider conditions ensuring the existence and stability of relationships between expected inputs and expected output and discuss their properties. Existence conditions for and relationships between higher-order moments are considered. An empirical implementation based on panel data for two manufacturing industries gives decomposition and simulation results for expected output and estimates of the aggregate parameters. Illustrations of approximation procedures and aggregation errors are also given.

Suggested Citation

  • Erik Biørn & Terje Skjerpen & Knut Reidar Wangen, 2003. "Parametric Aggregation of Random Coefficient Cobb-Douglas Production Functions: Evidence from Manufacturing Industries," Discussion Papers 342, Statistics Norway, Research Department.
    • Handle: RePEc:ssb:dispap:342
    as

    Download full text from publisher

    File URL: https://www.ssb.no/a/publikasjoner/pdf/DP/dp-342.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Klette, Tor Jakob & Griliches, Zvi, 1996. "The Inconsistency of Common Scale Estimators When Output Prices Are Unobserved and Endogenous," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(4), pages 343-361, July-Aug..
    2. Antle, John M, 1983. "Testing the Stochastic Structure of Production: A Flexible Moment-based Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(3), pages 192-201, July.
    3. Erik Biørn & Kjersti-Gro Lindquist & Terje Skjerpen, 2002. "Heterogeneity in Returns to Scale: A Random Coefficient Analysis with Unbalanced Panel Data," Journal of Productivity Analysis, Springer, vol. 18(1), pages 39-57, July.
    4. Erik Biørn & Kjersti-Gro Lindquist & Terje Skjerpen, 2000. "Micro Data On Capital Inputs: Attempts to Reconcile Stock and Flow Information," Discussion Papers 268, Statistics Norway, Research Department.
    5. Erik Biørn & Terje Skjerpen, 2002. "Aggregation and Aggregation Biases in Production Functions: A Panel Data Analysis of Translog Models," Discussion Papers 317, Statistics Norway, Research Department.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Erik Biørn & Terje Skjerpen & Knut R. Wangen, 2006. "Can Random Coefficient Cobb Douglas Production Functions be Aggregated to Similar Macro Functions?," Contributions to Economic Analysis, in: Panel Data Econometrics Theoretical Contributions and Empirical Applications, pages 229-258, Emerald Group Publishing Limited.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Erik Biørn & Terje Skjerpen & Knut R. Wangen, 2006. "Can Random Coefficient Cobb Douglas Production Functions be Aggregated to Similar Macro Functions?," Contributions to Economic Analysis, in: Panel Data Econometrics Theoretical Contributions and Empirical Applications, pages 229-258, Emerald Group Publishing Limited.
    2. Biorn, Erik & Skjerpen, Terje, 2004. "Aggregation biases in production functions: a panel data analysis of Translog models," Research in Economics, Elsevier, vol. 58(1), pages 31-57, March.
    3. Erik Biørn & Terje Skjerpen, 2002. "Aggregation and Aggregation Biases in Production Functions: A Panel Data Analysis of Translog Models," Discussion Papers 317, Statistics Norway, Research Department.
    4. Yuriy Gorodnichenko, 2007. "Using Firm Optimization to Evaluate and Estimate Returns to Scale," NBER Working Papers 13666, National Bureau of Economic Research, Inc.
    5. Ornaghi, Carmine, 2008. "Price deflators and the estimation of the production function," Economics Letters, Elsevier, vol. 99(1), pages 168-171, April.
    6. Andrew B. Bernard & Jonathan Eaton & J. Bradford Jensen & Samuel Kortum, 2003. "Plants and Productivity in International Trade," American Economic Review, American Economic Association, vol. 93(4), pages 1268-1290, September.
    7. Catherine Fuss & Angelos Theodorakopoulos, 2018. "Compositional Changes in Aggregate Productivity in an Era of Globalisation and Financial Crisis," Working Papers of VIVES - Research Centre for Regional Economics 627696, KU Leuven, Faculty of Economics and Business (FEB), VIVES - Research Centre for Regional Economics.
    8. Sauer, Johannes & Zilberman, David, 2009. "Innovation Behaviour At Farm Level – Selection And Identification," 83rd Annual Conference, March 30 - April 1, 2009, Dublin, Ireland 51073, Agricultural Economics Society.
    9. Francesco Serti & Chiara Tomasi, 2008. "Self-Selection and Post-Entry Effects of Exports: Evidence from Italian Manufacturing Firms," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 144(4), pages 660-694, December.
    10. Wu, Mingran & Zhao, Min & Wu, Zhaodan, 2019. "Evaluation of development level and economic contribution ratio of science and technology innovation in eastern China," Technology in Society, Elsevier, vol. 59(C).
    11. Matthias Arnold, Jens & Javorcik, Beata S., 2009. "Gifted kids or pushy parents? Foreign direct investment and plant productivity in Indonesia," Journal of International Economics, Elsevier, vol. 79(1), pages 42-53, September.
    12. Biorn, Erik & Hagen, Terje P. & Iversen, Tor & Magnussen, Jon, 2006. "Heterogeneity in Hospitals' Responses to a Financial Reform: A Random Coefficient Analysis of The Impact of Activity-Based Financing on Efficiency," MPRA Paper 8169, University Library of Munich, Germany.
    13. Kahyarara, Godius, 2021. "Macroeconomic Environment and Firm level Performance in Tanzania," African Journal of Economic Review, African Journal of Economic Review, vol. 9(2), April.
    14. Nicholas Bloom & Mark Schankerman & John Van Reenen, 2013. "Identifying Technology Spillovers and Product Market Rivalry," Econometrica, Econometric Society, vol. 81(4), pages 1347-1393, July.
    15. Sabien Dobbelaere & Jacques Mairesse, 2010. "Comparing Micro-evidence on Rent Sharing from Three Different Approaches," NBER Working Papers 16220, National Bureau of Economic Research, Inc.
    16. Chiara Criscuolo & Ralf Martin, 2009. "Multinationals and U.S. Productivity Leadership: Evidence from Great Britain," The Review of Economics and Statistics, MIT Press, vol. 91(2), pages 263-281, May.
    17. Gregory Corcos & Massimo Del Gatto & Giordano Mion & Gianmarco I.P. Ottaviano, 2012. "Productivity and Firm Selection: Quantifying the ‘New’ Gains from Trade," Economic Journal, Royal Economic Society, vol. 122(561), pages 754-798, June.
    18. Michał Gradzewicz, 2022. "How do firms respond to demand and supply shocks?," KAE Working Papers 2022-075, Warsaw School of Economics, Collegium of Economic Analysis.
    19. Hassan, Rashid M. & D'Silva, Brian & Hallam, A., 1989. "Normative Supply Response Analysis under Production Uncertainty: Irrigated Multicrop Farming Sector of Sudan," 1989 Occasional Paper Series No. 5 197677, International Association of Agricultural Economists.
    20. Kohler, Wilhelm & Smolka, Marcel, 2014. "Global sourcing and firm selection," Economics Letters, Elsevier, vol. 124(3), pages 411-415.

    More about this item

    Keywords

    Aggregation. Productivity. Cobb-Douglas. Log-normal distribution. Random coefficients. Panel data.;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ssb:dispap:342. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: L Maasø (email available below). General contact details of provider: https://edirc.repec.org/data/ssbgvno.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.