IDEAS home Printed from
   My bibliography  Save this paper

Estimation Of Efficient Regression Models For Applied Agricultural Economics Research


  • Ramirez, Octavio A.
  • Misra, Sukant K.
  • Nelson, Jeannie


This paper proposes and explores the use of a partially adaptive estimation technique to improve the reliability of the inferences made from multiple regression models when the dependent variable is not normally distributed. The relevance of this technique for agricultural economics research is evaluated through Monte Carlo simulation and two mainstream applications: A time-series analysis of agricultural commodity prices and an empirical model of the West Texas cotton basis. It is concluded that, given non-normality, this technique can substantially reduce the magnitude of the standard errors of the slope parameter estimators in relation to OLS, GLS and other least squares based estimation procedures, in practice, allowing for more precise inferences about the existence, sign and magnitude of the effects of the independent variables on the dependent variable of interest. In addition, the technique produces confidence intervals for the dependent variable forecasts that are more efficient and consistent with the observed data. Key Words: Efficient regression models, partially adaptive estimation, non-normality, skewness, heteroskedasticity, autocorrelation.

Suggested Citation

  • Ramirez, Octavio A. & Misra, Sukant K. & Nelson, Jeannie, 2002. "Estimation Of Efficient Regression Models For Applied Agricultural Economics Research," 2002 Annual meeting, July 28-31, Long Beach, CA 19904, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  • Handle: RePEc:ags:aaea02:19904

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Krinsky, Itzhak & Robb, A Leslie, 1986. "On Approximating the Statistical Properties of Elasticities," The Review of Economics and Statistics, MIT Press, vol. 68(4), pages 715-719, November.
    2. McDonald, James B. & Newey, Whitney K., 1988. "Partially Adaptive Estimation of Regression Models via the Generalized T Distribution," Econometric Theory, Cambridge University Press, vol. 4(03), pages 428-457, December.
    3. Newey, Whitney K., 1988. "Adaptive estimation of regression models via moment restrictions," Journal of Econometrics, Elsevier, vol. 38(3), pages 301-339, July.
    4. Seamon, V. Frederick & Kahl, Kandice H., 2000. "An Analysis Of Factors Affecting The Regional Cotton Basis," 2000 Conference, April 17-18 2000, Chicago, Illinois 18924, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    5. McDonald, James B., 1989. "Partially adaptive estimation of ARMA time series models," International Journal of Forecasting, Elsevier, vol. 5(2), pages 217-230.
    Full references (including those not matched with items on IDEAS)


    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:aaea02:19904. 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: .

    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.