IDEAS home Printed from https://ideas.repec.org/p/ags/aesc09/51063.html
   My bibliography  Save this paper

A Nonparametric Kernel Representation of the Agricultural Production Function: Implications for Economic Measures of Technology

Author

Listed:
  • Livanis, Grigorios T.
  • Salois, Matthew J.
  • Moss, Charles B.

Abstract

The issue of production function estimation has received recent attention, particularly in agricultural economics with the advent of precision farming. Yet, the evidence to date is far from unanimous on the proper form of the production function. This paper reexamines the use of the primal production function framework using nonparametric regression techniques. Specifically, the paper demonstrates how a nonparametric regression based on a kernel density estimator can be used to estimate a production function using data on corn production from Illinois and Indiana. Nonparametric results are compared to common parametric specifications using the Nadaraya-Watson kernel regression estimator. The parametric and nonparametric forms are also compared in terms of describing the true technology of the firm by obtaining measures of the elasticity of scale and the marginal physical product through nonparametric estimation of the gradient of the production surface. Finally, the elasticities of substitution are compared between both parametric and nonparametric representations.

Suggested Citation

  • Livanis, Grigorios T. & Salois, Matthew J. & Moss, Charles B., 2009. "A Nonparametric Kernel Representation of the Agricultural Production Function: Implications for Economic Measures of Technology," 83rd Annual Conference, March 30-April 1, 2009, Dublin, Ireland 51063, Agricultural Economics Society.
  • Handle: RePEc:ags:aesc09:51063
    as

    Download full text from publisher

    File URL: http://purl.umn.edu/51063
    Download Restriction: no

    References listed on IDEAS

    as
    1. Daniel J. Henderson & Daniel L. Millimet, 2007. "Pollution Abatement Costs and Foreign Direct Investment Inflows to U.S. States: A Nonparametric Reassessment," The Review of Economics and Statistics, MIT Press, vol. 89(1), pages 178-183, February.
    2. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, number 8355, June.
    3. Hardle, Wolfgang & Linton, Oliver, 1986. "Applied nonparametric methods," Handbook of Econometrics,in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 38, pages 2295-2339 Elsevier.
    4. Martin van Ittersum & Ada Wossink, 2006. "Integrating Agronomic Principles into Production Function Specification: A Dichotomy of Growth Inputs and Facilitating Inputs," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(1), pages 203-214.
    5. Babcock, Bruce A., 1992. "Effects of Uncertainty on Optimal Nitrogen Applications (The)," Staff General Research Papers Archive 10588, Iowa State University, Department of Economics.
    6. Adonis Yatchew, 1998. "Nonparametric Regression Techniques in Economics," Journal of Economic Literature, American Economic Association, vol. 36(2), pages 669-721, June.
    7. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643, March.
    8. Joseph C. Cooper, 2000. "Nonparametric and Semi-Nonparametric Recreational Demand Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(2), pages 451-462.
    9. Daniel J. Henderson & Subal C. Kumbhakar, 2006. "Public and Private Capital Productivity Puzzle: A Nonparametric Approach," Southern Economic Journal, Southern Economic Association, vol. 73(1), pages 219-232, July.
    10. John DiNardo & Justin L. Tobias, 2001. "Nonparametric Density and Regression Estimation," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 11-28, Fall.
    11. Garth Holloway & Quirino Paris, 2002. "Production Efficiency in the von Liebig Model," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(5), pages 1271-1278.
    12. Babcock, Bruce A. & Blackmer, A. M., 1994. "Ex Post Relationship Between Growing Conditions and Nitrogen Fertilizer Levels (The)," Staff General Research Papers Archive 10582, Iowa State University, Department of Economics.
    13. Ullah, Aman, 1988. "Nonparametric Estimation and Hypothesis Testing in Econometric Models," Empirical Economics, Springer, vol. 13(3/4), pages 223-249.
    14. Hardle, Wolfgang & Linton, Oliver, 1986. "Applied nonparametric methods," Handbook of Econometrics,in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 38, pages 2295-2339 Elsevier.
    15. A. Yatchew, 2000. "Scale economies in electricity distribution: a semiparametric analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(2), pages 187-210.
    16. Charles Moss & Troy Schmitz, 2006. "A semiparametric estimator of the Zellner production function for corn: fitting the univariate primal," Applied Economics Letters, Taylor & Francis Journals, vol. 13(13), pages 863-867.
    17. Hsiao, Cheng & Li, Qi & Racine, Jeffrey S., 2007. "A consistent model specification test with mixed discrete and continuous data," Journal of Econometrics, Elsevier, vol. 140(2), pages 802-826, October.
    18. Robert G. Chambers & Erik Lichtenberg, 1996. "A Nonparametric Approach to the von Liebig-Paris Technology," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, pages 373-386.
    19. Alan P. Ker & Barry K. Goodwin, 2000. "Nonparametric Estimation of Crop Insurance Rates Revisited," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(2), pages 463-478.
    20. Mundlak, Yair, 1996. "Production Function Estimation: Reviving the Primal," Econometrica, Econometric Society, vol. 64(2), pages 431-438, March.
    21. Peter Berck & Jacqueline Geoghegan & Stephen Stohs, 2000. "A Strong Test of the von Liebig Hypothesis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(4), pages 948-955.
    22. Rilstone, Paul, 1991. "Nonparametric Hypothesis Testing with Parametric Rates of Convergence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 32(1), pages 209-227, February.
    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. Tomasz Gerard Czekaj & Arne Henningsen, 2012. "Comparing Parametric and Nonparametric Regression Methods for Panel Data: the Optimal Size of Polish Crop Farms," IFRO Working Paper 2012/12, University of Copenhagen, Department of Food and Resource Economics.

    More about this item

    Keywords

    nonparametric regression; nonparametric derivatives; Gaussian kernel; optimization techniques; production function; Production Economics; C14; C15; C16; C61; Q12;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets

    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:ags:aesc09:51063. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search). General contact details of provider: http://edirc.repec.org/data/aesukea.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.