IDEAS home Printed from https://ideas.repec.org/a/ags/nejare/29030.html

Investment Behavior And Energy Conservation

Author

Listed:
  • LaDue, Eddy L.
  • Miller, Lynn H.
  • Kwiatkowski, Joseph H.

Abstract

Binary logit and bivariate probit models were used to investigate the investment behavior of farmers relative to two energy-conserving assets, heat-recovery systems and precoolers. The bivariate probit procedure was useful in correcting for self-selectivity bias. Holdout samples and cross-validation procedures were used to develop true model statistics. Farm size, educational level of the operator, and the type of milking system in use were the important factors influencing investment behavior.

Suggested Citation

  • LaDue, Eddy L. & Miller, Lynn H. & Kwiatkowski, Joseph H., 1990. "Investment Behavior And Energy Conservation," Northeastern Journal of Agricultural and Resource Economics, Northeastern Agricultural and Resource Economics Association, vol. 19(2), pages 1-10, October.
  • Handle: RePEc:ags:nejare:29030
    DOI: 10.22004/ag.econ.29030
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/29030/files/19020099.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.29030?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
    ---><---

    References listed on IDEAS

    as
    1. Keileher, Michael J. & Bills, Nelson L., 1989. "Statistical Summary of the 1987 Farm Management and Energy Survey," Research Bulletins 183304, Cornell University, Department of Applied Economics and Management.
    2. Lowell Hill & Paul Kau, 1973. "Application of Multivariate Probit to a Threshold Model of Grain Dryer Purchasing Decisions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 55(1), pages 19-27.
    3. LaDue, Eddy L. & Miller, Lynn H. & Kwiatkowski, Joseph H., 1989. "An Analysis of Alternate Micro Level Models of Investment Behavior," Working Papers 178722, Cornell University, Department of Applied Economics and Management.
    4. Gustafson, Cole R. & Barry, Peter J. & Sonka, Steven T., 1986. "Machinery Investment Decisions By Cash Grain Farmers In Illinois," 1986 Regional Committee NC-161, October 7-8, 1986, St. Paul, Minnesota 127216, Regional Research Committee NC-1014: Agricultural and Rural Finance Markets in Transition.
    5. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. "Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-291, March.
    6. Johnson, Thomas G. & Brown, William J. & O'Grady, Kevin, 1985. "A Multivariate Analysis Of Factors Influencing Farm Machinery Purchase Decisions," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 10(2), pages 1-13, December.
    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. Matthew Smith & Francisco Alvarez, 2022. "Predicting Firm-Level Bankruptcy in the Spanish Economy Using Extreme Gradient Boosting," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 263-295, January.
    2. Suzan Hol, 2006. "The influence of the business cycle on bankruptcy probability," Discussion Papers 466, Statistics Norway, Research Department.
    3. Harlan Platt & Marjorie Platt, 2002. "Predicting corporate financial distress: Reflections on choice-based sample bias," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 26(2), pages 184-199, June.
    4. Łukasz Postek & Michał Thor, 2020. "Modele predykcji bankructwa i ich zastosowanie dla rynku NewConnect," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 1, pages 109-137.
    5. Dimitras, A. I. & Slowinski, R. & Susmaga, R. & Zopounidis, C., 1999. "Business failure prediction using rough sets," European Journal of Operational Research, Elsevier, vol. 114(2), pages 263-280, April.
    6. Beynon, Malcolm J. & Peel, Michael J., 2001. "Variable precision rough set theory and data discretisation: an application to corporate failure prediction," Omega, Elsevier, vol. 29(6), pages 561-576, December.
    7. Li-Chiu Chi & Tseng-Chung Tang, 2006. "Bankruptcy Prediction: Application of Logit Analysis in Export Credit Risks," Australian Journal of Management, Australian School of Business, vol. 31(1), pages 17-27, June.
    8. Michal Karas & Mária Režòáková, 2023. "A novel approach to estimating the debt capacity of European SMEs," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 18(2), pages 551-581, June.
    9. Kattan, MW & Cooper, RB, 1998. "The predictive accuracy of computer-based classification decision techniques.A review and research directions," Omega, Elsevier, vol. 26(4), pages 467-482, August.
    10. Maria H. Kim & Graham Partington, 2015. "Dynamic forecasts of financial distress of Australian firms," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 135-160, February.
    11. repec:hal:cepnwp:hal-01922256 is not listed on IDEAS
    12. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Forecast bankruptcy using a blend of clustering and MARS model - Case of US banks," Post-Print halshs-01314553, HAL.
    13. Ward, Felix, 2014. "Spotting the Danger Zone - Forecasting Financial Crises with Classification Tree Ensembles and Many Predictors," Bonn Econ Discussion Papers 01/2014, University of Bonn, Bonn Graduate School of Economics (BGSE).
    14. Pablo de Llano Monelos & Manuel Rodríguez López & Carlos Piñeiro Sánchez, 2013. "Bankruptcy Prediction Models in Galician companies. Application of Parametric Methodologies and Artificial Intelligence," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 117-136.
    15. Kurt M. Fanning & Kenneth O. Cogger, 1994. "A Comparative Analysis of Artificial Neural Networks Using Financial Distress Prediction," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 3(4), pages 241-252, December.
    16. Demyanyk, Yuliya & Hasan, Iftekhar, 2010. "Financial crises and bank failures: A review of prediction methods," Omega, Elsevier, vol. 38(5), pages 315-324, October.
    17. Barboza, Flavio & Altman, Edward, 2024. "Predicting financial distress in Latin American companies: A comparative analysis of logistic regression and random forest models," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    18. Jacobson, Tor & Linde, Jesper & Roszbach, Kasper, 2005. "Exploring interactions between real activity and the financial stance," Journal of Financial Stability, Elsevier, vol. 1(3), pages 308-341, April.
    19. Willis, Cleve E. & Crawford, Charles O., 1976. "Methodological Issues In Analyses Of Use And Adequacy Of Community Services In Rural Areas: With An Application To Legal Services In The Northeast," Northeastern Journal of Agricultural and Resource Economics, Northeastern Agricultural and Resource Economics Association, vol. 0(Number 2), pages 1-12, October.
    20. Casado Yusta, Silvia & Nœ–ez Letamendía, Laura & Pacheco Bonrostro, Joaqu’n Antonio, 2018. "Predicting Corporate Failure: The GRASP-LOGIT Model || Predicci—n de la quiebra empresarial: el modelo GRASP-LOGIT," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 294-314, Diciembre.
    21. Ruey-Ching Hwang & K. F. Cheng & Jack C. Lee, 2007. "A semiparametric method for predicting bankruptcy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(5), pages 317-342.

    More about this item

    Keywords

    ;

    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:nejare:29030. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/nareaea.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.