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Improving Estimates of Economic Parameters by Use of Ridge Regression with Production Function Applications

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  • William G. Brown
  • Bruce R. Beattie

Abstract

Ridge regression is a promising alternative to deletion of relevant variables for alleviating multicollinearity and can provide smaller mean square error estimates than unbiased methods such as OLS. However, ridge estimates can also be unreliable and misleading under certain conditions. To avoid erroneous conclusions from ridge regression, some prior knowledge about the true regression coefficients is helpful. A theorem on expected bias implies that ridge regression will give much better results for some economic models, such as certain production functions, than for others because of smaller expected bias.

Suggested Citation

  • William G. Brown & Bruce R. Beattie, 1975. "Improving Estimates of Economic Parameters by Use of Ridge Regression with Production Function Applications," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 57(1), pages 21-32.
    • Handle: RePEc:oup:ajagec:v:57:y:1975:i:1:p:21-32.
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    File URL: http://hdl.handle.net/10.2307/1238836
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    Cited by:

    1. Norton, George W. & Coffey, Joseph D. & Frye, E. Berrier, 1984. "Estimating Returns To Agricultural Research, Extension, And Teaching At The State Level," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 16(1), pages 1-8, July.
    2. Lita da Silva, João, 2014. "Some strong consistency results in stochastic regression," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 220-226.
    3. Pan, Shihua, 1990. "The microfoundations of mixed system of planning and markets: some theoretical considerations and an empirical analysis of the Chinese agriculture," ISU General Staff Papers 1990010108000010876, Iowa State University, Department of Economics.
    4. Catalina Garcia & José Pérez & José Liria, 2011. "The raise method. An alternative procedure to estimate the parameters in presence of collinearity," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(2), pages 403-423, February.
    5. Semprevio, Ralph R. & Capps, Oral Jr., 1981. "Detection And Treatment Of Multicollinearity In Simultaneous Systems Of Equations," 1981 Annual Meeting, July 26-29, Clemson, South Carolina 279383, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    6. Madariaga, Bruce & McConnell, Kenneth E., 1984. "Value Of Irrigation Water In The Middle Atlantic States: An Econometric Approach," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 16(2), pages 1-8, December.
    7. Willis, Cleve E. & Perlack, Robert D., 1978. "Multicollinearity: Effects, Symptoms, And Remedies," Northeastern Journal of Agricultural and Resource Economics, Northeastern Agricultural and Resource Economics Association, vol. 0(Number 1), pages 1-7, April.
    8. Gollehon, Noel R. & Moore, Michael R. & Negri, Donald H., 1990. "Production Functions Of Western Irrigated Crops," 1990 Annual meeting, August 5-8, Vancouver, Canada 270992, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    9. Willis, Cleve E. & Perlack, Robert D., 1978. "Multicollinearity: Effects, Symptoms, And Remedies," Journal of the Northeastern Agricultural Economics Council, Northeastern Agricultural and Resource Economics Association, vol. 7(1), pages 1-7, April.
    10. Lynne, Gary D., 1978. "Issues And Problems In Agricultural Water Demand Estimation From Secondary Data Sources," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 10(2), pages 1-6, December.
    11. John Deegan, 1979. "Constructing statistical models of social processes," Quality & Quantity: International Journal of Methodology, Springer, vol. 13(2), pages 97-119, April.
    12. Burt, Oscar R. & Frank, Michael D. & Beattie, Bruce R., 1987. "Prior Information And Heuristic Ridge Regression For Production Function Estimation," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 12(2), pages 1-9, December.
    13. Birgit Nahrstedt & Henning P. Jørgensen & Ayoe Hoff, 2002. "Estimation of Production Functions on Fishery: A Danish Survey," Working Papers 33/02, University of Southern Denmark, Department of Sociology, Environmental and Business Economics.

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