IDEAS home Printed from https://ideas.repec.org/a/taf/emetrv/v19y2000i3p367-389.html
   My bibliography  Save this article

Flexible panel data models for risky production technologies with an application to salmon aquaculture

Author

Listed:
  • Ragner Tveterås
  • G. H. Wan

Abstract

Primal panel data models of production risk are estimated, using more flexible specifications than has previously been the practice. Production risk has important implications for the analysis of technology adoption and technical efficiency, since risk averse producers will take into account both the mean and variance of output when ranking alternative technologies. Hence, one should estimate technical change separately for the deterministic part and the risk part of thetechnology.

Suggested Citation

  • Ragner Tveterås & G. H. Wan, 2000. "Flexible panel data models for risky production technologies with an application to salmon aquaculture," Econometric Reviews, Taylor & Francis Journals, vol. 19(3), pages 367-389.
    • Handle: RePEc:taf:emetrv:v:19:y:2000:i:3:p:367-389
    DOI: 10.1080/07474930008800477
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/07474930008800477
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/07474930008800477?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wan, Guang H & Griffiths, William E & Anderson, Jock R, 1992. "Using Panel Data to Estimate Risk Effects in Seemingly Unrelated Production Functions," Empirical Economics, Springer, vol. 17(1), pages 35-49.
    2. Greg Traxler & Jose Falck-Zepeda & J.I. Ortiz-Monasterio R. & Ken Sayre, 1995. "Production Risk and the Evolution of Varietal Technology," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(1), pages 1-7.
    3. Just, Richard E. & Pope, Rulon D., 1978. "Stochastic specification of production functions and economic implications," Journal of Econometrics, Elsevier, vol. 7(1), pages 67-86, February.
    4. Richard E. Just & Rulon D. Pope, 1979. "Production Function Estimation and Related Risk Considerations," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 61(2), pages 276-284.
    5. Blair, Roger D. & Lusky, Rafael, 1975. "A note on the influence of uncertainty on estimation of production function models," Journal of Econometrics, Elsevier, vol. 3(4), pages 391-394, November.
    6. Atanu Saha & Arthur Havenner & Hovav Talpaz, 1997. "Stochastic production function estimation: small sample properties of ML versus FGLS," Applied Economics, Taylor & Francis Journals, vol. 29(4), pages 459-469.
    7. Cornwell, Christopher & Schmidt, Peter, 1992. "Models for Which the MLE and the Conditional MLE Coincide," Empirical Economics, Springer, vol. 17(1), pages 67-75.
    8. Arne Hallam & Rashid M. Hassan & B. D'Silva, 1989. "Measuring Stochastic Technology for the Multi-product Firm: The Irrigated Farms of Sudan," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 37(3), pages 495-512, November.
    9. Harvey, A C, 1976. "Estimating Regression Models with Multiplicative Heteroscedasticity," Econometrica, Econometric Society, vol. 44(3), pages 461-465, May.
    10. Paul Driscoll & Jeff Alwang & Anya McGuirk, 1992. "Testing Hypotheses of Functional Structure: Some Rules for Determining Flexibility of Restricted Production Models," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 74(1), pages 100-108.
    11. Bharat Ramaswami, 1992. "Production Risk and Optimal Input Decisions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 74(4), pages 860-869.
    12. Diewert, W E, 1971. "An Application of the Shephard Duality Theorem: A Generalized Leontief Production Function," Journal of Political Economy, University of Chicago Press, vol. 79(3), pages 481-507, May-June.
    13. ZELLNER, Arnold & KMENTA, Jan & DREZE, Jacques H., 1966. "Specification and estimation of Cobb-Douglas production function models," LIDAM Reprints CORE 12, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    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. Nolan, Elizabeth & Santos, Paulo, 2011. "Risk premiums and GM traits," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103692, Agricultural and Applied Economics Association.
    2. Ragnar Tveteras & Ola Flaten & Gudbrand Lien, 2011. "Production risk in multi-output industries: estimates from Norwegian dairy farms," Applied Economics, Taylor & Francis Journals, vol. 43(28), pages 4403-4414.
    3. Raju Guntukula & Phanindra Goyari, 2020. "Climate Change Effects on the Crop Yield and Its Variability in Telangana, India," Studies in Microeconomics, , vol. 8(1), pages 119-148, June.
    4. Holst, Rainer & Yu, Xiaohua, 2010. "Climate Change And Production Risk In Chinese Aquaculture," 2010: Climate Change in World Agriculture: Mitigation, Adaptation, Trade and Food Security, June 2010, Stuttgart-Hohenheim, Germany 91275, International Agricultural Trade Research Consortium.

    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. Ragnar Tveteras & Ola Flaten & Gudbrand Lien, 2011. "Production risk in multi-output industries: estimates from Norwegian dairy farms," Applied Economics, Taylor & Francis Journals, vol. 43(28), pages 4403-4414.
    2. Asche, Frank & Tveteras, Ragnar, 1999. "Modeling Production Risk With A Two-Step Procedure," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 24(2), pages 1-16, December.
    3. Antti Saastamoinen, 2015. "Heteroscedasticity Or Production Risk? A Synthetic View," Journal of Economic Surveys, Wiley Blackwell, vol. 29(3), pages 459-478, July.
    4. Ragnar Tveteros, 1999. "Production Risk and Productivity Growth: Some Findings for Norwegian Salmon Aquaculture," Journal of Productivity Analysis, Springer, vol. 12(2), pages 161-179, September.
    5. Do, Huu-Luat & Dang Thuy, Truong, 2022. "Productivity response and production risk: A study of mangrove forest effects in aquaculture in the Mekong River Delta," Ecological Economics, Elsevier, vol. 194(C).
    6. Asche, Frank & Guttormsen, Atle G. & Roll, Kristin H., 2006. "Modelling Production Risk in Small Scale Subsistence Agriculture," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25574, International Association of Agricultural Economists.
    7. Atanu Saha & Arthur Havenner & Hovav Talpaz, 1997. "Stochastic production function estimation: small sample properties of ML versus FGLS," Applied Economics, Taylor & Francis Journals, vol. 29(4), pages 459-469.
    8. Nolan, Elizabeth & Santos, Paulo, 2012. "Insurance premiums and GM traits," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 125942, International Association of Agricultural Economists.
    9. Gupta, Shreekant & Sen, Partha & Verma, Saumya, 2016. "Impact of Climate Change on Foodgrain Yields in India," CEI Working Paper Series 2015-9, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University.
    10. Mitchell, Paul David, 1999. "The theory and practice of green insurance: insurance to encourage the adoption of corn rootworm IPM," ISU General Staff Papers 1999010108000013154, Iowa State University, Department of Economics.
    11. Peter E. Rossi, 1984. "Stochastic Specification of Cost and Production Relationships," Discussion Papers 616, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    12. Jutta Roosen & David A. Hennessy, 2003. "Tests for the Role of Risk Aversion on Input Use," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(1), pages 30-43.
    13. Murat Isik & Madhu Khanna, 2003. "Stochastic Technology, Risk Preferences, and Adoption of Site-Specific Technologies," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(2), pages 305-317.
    14. De Nova, Carolina Carbajal, 2021. "Synthetic data. A novel proposed method for applied risk management," 95th Annual Conference, March 29-30, 2021, Warwick, UK (Hybrid) 311085, Agricultural Economics Society - AES.
    15. Kim, Kwansoo & Chavas, Jean-Paul, 2003. "Technological change and risk management: an application to the economics of corn production," Agricultural Economics, Blackwell, vol. 29(2), pages 125-142, October.
    16. Catherine Benjamin & Ewen Gallic, 2017. "Effects of Climate Change on Agriculture: a European case study," Economics Working Paper Archive (University of Rennes & University of Caen) 2017-16, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.
    17. Hassan, Rashid M. & Hallam, Arne & D'Silva, B., 1988. "Stochastic Technology in a Programming Framework: A Generalized E. V. Model," 1988 Annual Meeting, August 1-3, Knoxville, Tennessee 270212, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    18. Alain Carpentier & Robert D. Weaver, 1996. "Assessment of producers' attitude toward risk and information using panel data : the example of pesticide use in the French crop sector," Post-Print hal-01931607, HAL.
    19. Rulon D. Pope & Richard E. Just, 1977. "On The Competitive Firm Under Production Uncertainty," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 21(2), pages 111-118, August.
    20. Tai-Hsin Huang, 2004. "Empirical estimation of production risk using a cost function with panel data," Applied Economics Letters, Taylor & Francis Journals, vol. 11(5), pages 297-301.

    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:taf:emetrv:v:19:y:2000:i:3:p:367-389. 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: the person in charge (email available below). General contact details of provider: http://www.tandfonline.com/LECR20 .

    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.