IDEAS home Printed from https://ideas.repec.org/p/rtv/ceisrp/399.html
   My bibliography  Save this paper

Theory and Practice of TFP Estimation: the Control Function Approach Using Stata

Author

Abstract

Alongside Instrumental Variable (IV) and Fixed Effects (FE), the Control Function (CF) approach is the most widely used in production function estimation. Olley-Pakes, Levinsohn-Petrin, Ackerberg-Caves-Frazer have all contributed to the literature proposing two-steps estimation procedures, while Wooldridge showed how to perform a consistent estimation within a single step GMM framework. In this paper we propose a new estimator, based on Wooldridge's, using dynamic panel instruments à la Blundell-Bond and we evaluate its performance by Monte Carlo simulations. We also present a new Stata module - prodest - for production function estimation, show its main features and key strengths in a comparative analysis with other available user-written commands. Lastly, we provide evidence of the numerical challenges faced when using OP/LP estimators with ACF correction in empirical applications and document how the GMM estimates vary depending on the optimization/starting points used.

Suggested Citation

  • Vincenzo Mollisi & Gabriele Rovigatti, 2017. "Theory and Practice of TFP Estimation: the Control Function Approach Using Stata," CEIS Research Paper 399, Tor Vergata University, CEIS, revised 14 Feb 2017.
    • Handle: RePEc:rtv:ceisrp:399
    as

    Download full text from publisher

    File URL: https://ceistorvergata.it/RePEc/rpaper/RP399.pdf
    File Function: Main text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stephen Bond & Måns Söderbom, 2005. "Adjustment Costs and the Identification of Cobb Douglas Production Functions," Economics Papers 2005-W04, Economics Group, Nuffield College, University of Oxford.
    2. Christopher R. Knittel & Konstantinos Metaxoglou, 2014. "Estimation of Random-Coefficient Demand Models: Two Empiricists' Perspective," The Review of Economics and Statistics, MIT Press, vol. 96(1), pages 34-59, March.
    3. Olley, G Steven & Pakes, Ariel, 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry," Econometrica, Econometric Society, vol. 64(6), pages 1263-1297, November.
    4. Amil Petrin & Brian P. Poi & James Levinsohn, 2004. "Production function estimation in Stata using inputs to control for unobservables," Stata Journal, StataCorp LP, vol. 4(2), pages 113-123, June.
    5. Mahmut Yasar & Rafal Raciborski & Brian Poi, 2008. "Production function estimation in Stata using the Olley and Pakes method," Stata Journal, StataCorp LP, vol. 8(2), pages 221-231, June.
    6. Daniel A. Ackerberg & Kevin Caves & Garth Frazer, 2015. "Identification Properties of Recent Production Function Estimators," Econometrica, Econometric Society, vol. 83, pages 2411-2451, November.
    7. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 70(2), pages 317-341.
    8. Wooldridge, Jeffrey M., 1996. "Estimating systems of equations with different instruments for different equations," Journal of Econometrics, Elsevier, vol. 74(2), pages 387-405, October.
    9. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    10. Marc J. Melitz, 2003. "The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity," Econometrica, Econometric Society, vol. 71(6), pages 1695-1725, November.
    11. Wooldridge, Jeffrey M., 2009. "On estimating firm-level production functions using proxy variables to control for unobservables," Economics Letters, Elsevier, vol. 104(3), pages 112-114, September.
    12. Stephen Bond & Måns Söderbom, 2005. "Adjustment Costs and the Identification of Cobb Douglas Production Functions," Economics Series Working Papers 2005-W04, University of Oxford, Department of Economics.
    Full references (including those not matched with items on IDEAS)

    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. Simon Pröll & Giannis Karagiannis & Klaus Salhofer, 2019. "Advertising and Markups: The Case of the German Brewing Industry," Working Papers 732019, University of Natural Resources and Life Sciences, Vienna, Department of Economics and Social Sciences, Institute for Sustainable Economic Development.
    2. Petrick, Matin & Kloss, Mathias, 2013. "Identifying Factor Productivity from Micro-data: The case of EU agriculture," Factor Markets Working Papers 144, Centre for European Policy Studies.
    3. Benoît Mahy & François Rycx & Guillaume Vermeylen & Mélanie Volral, 2018. "Productivity, wages and profits: Does firms’ position in the value chain matter?," Working Paper Research 358, National Bank of Belgium.
    4. Hu, Yingyao & Huang, Guofang & Sasaki, Yuya, 2020. "Estimating production functions with robustness against errors in the proxy variables," Journal of Econometrics, Elsevier, vol. 215(2), pages 375-398.
    5. Bournakis, Ioannis & Mallick, Sushanta, 2018. "TFP estimation at firm level: The fiscal aspect of productivity convergence in the UK," Economic Modelling, Elsevier, vol. 70(C), pages 579-590.
    6. repec:zbw:iamodp:271870 is not listed on IDEAS
    7. repec:zbw:inwedp:732019 is not listed on IDEAS
    8. Peng, Jiaying & Xie, Rui & Ma, Chunbo & Fu, Yang, 2021. "Market-based environmental regulation and total factor productivity: Evidence from Chinese enterprises," Economic Modelling, Elsevier, vol. 95(C), pages 394-407.
    9. Heesun Jang & Hyunhee Kim & Hojeong Park, 2020. "Spatiotemporal analysis of Korean ginseng farm productivity," Journal of Productivity Analysis, Springer, vol. 53(1), pages 69-78, February.
    10. Bruno Merlevede & Angelos Theodorakopoulos, 2016. "Productivity effects from inter-industry offshoring and inshoring: Firm-level evidence from Belgium," FIW Working Paper series 165, FIW.
    11. López Noria Gabriela, 2021. "The Impact of Trade Liberalization on the Mexican Automobile Industry: Evidence from the First 20 Years of NAFTA," Working Papers 2021-10, Banco de México.
    12. Axel Demenet, 2016. "Does Managerial Capital also Matter Among Micro and Small Firms in Developing Countries?," Working Papers DT/2016/12, DIAL (Développement, Institutions et Mondialisation).
    13. Axel Demenet & Quynh Hoang, 2018. "How important are management practices for the productivity of small and medium enterprises?," WIDER Working Paper Series 69, World Institute for Development Economic Research (UNU-WIDER).
    14. Dobbelaere, Sabien & Kiyota, Kozo & Mairesse, Jacques, 2015. "Product and labor market imperfections and scale economies: Micro-evidence on France, Japan and the Netherlands," Journal of Comparative Economics, Elsevier, vol. 43(2), pages 290-322.
    15. Lu, Yunguo & Zhang, Lin, 2022. "National mitigation policy and the competitiveness of Chinese firms," Energy Economics, Elsevier, vol. 109(C).
    16. David B. Audretsch & Alexander S. Kritikos & Alexander Schiersch, 2020. "Microfirms and innovation in the service sector," Small Business Economics, Springer, vol. 55(4), pages 997-1018, December.
    17. François Rycx & Yves Saks & Ilan Tojerow, 2015. "Does Education Raise Productivity and Wages Equally? The Moderating Roles of Age, Gender and Industry," Working Paper Research 281, National Bank of Belgium.
    18. Audretsch, David & Hafenstein, Marian & Kritikos, Alexander S. & Schiersch, Alexander, 2018. "Firm Size and Innovation in the Service Sector," IZA Discussion Papers 12035, Institute of Labor Economics (IZA).
    19. Peter Tóth, 2014. "To What Extent Can Czech Exporters Cushion Exchange Rate Shocks through Imported Inputs?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 3, pages 74-93.
    20. Segundo Camino‐Mogro & Natalia Bermudez‐Barrezueta, 2021. "Productivity determinants in the construction sector in emerging country: New evidence from Ecuadorian firms," Review of Development Economics, Wiley Blackwell, vol. 25(4), pages 2391-2413, November.
    21. Markus Eberhardt & Christian Helmers, 2010. "Untested Assumptions and Data Slicing: A Critical Review of Firm-Level Production Function Estimators," Economics Series Working Papers 513, University of Oxford, Department of Economics.
    22. Paul Schrimpf & Michio Suzuki & Hiroyuki Kasahara, 2015. "Identification and Estimation of Production Function with Unobserved Heterogeneity," 2015 Meeting Papers 924, Society for Economic Dynamics.

    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:rtv:ceisrp:399. 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: Barbara Piazzi (email available below). General contact details of provider: https://edirc.repec.org/data/csrotit.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.