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Applying Principal Components Regression Analysis to Time Series Demand Estimation

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  • Sanint, Luis R.

Abstract

Demand functions for rice in Colombia and Venezuela, estimated by means of ordinary least square, were unsatisfactory because of problems with multicollinearity An alternative approach, principal components regression, was tried Results showed that principal components regression estimates were more consistent with theoretical expectations and were statistically more significant The cost of these gains was that the coefficients were biased However, the mean-square-error tests indicated that the reduction in variance outweighed the loss due to bias

Suggested Citation

  • Sanint, Luis R., 1982. "Applying Principal Components Regression Analysis to Time Series Demand Estimation," Journal of Agricultural Economics Research, United States Department of Agriculture, Economic Research Service, vol. 34(3), pages 1-7, July.
    • Handle: RePEc:ags:uersja:148826
    DOI: 10.22004/ag.econ.148826
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    Citations

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    Cited by:

    1. Trueblood, Michael Alan, 1991. "Agricultural Production Functions Estimated From Aggregate Intercountry Observations: A Selected Survey," Staff Reports 278560, United States Department of Agriculture, Economic Research Service.
    2. Ron Johnston & Kelvyn Jones & David Manley, 2018. "Confounding and collinearity in regression analysis: a cautionary tale and an alternative procedure, illustrated by studies of British voting behaviour," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(4), pages 1957-1976, July.
    3. Kevin T. McNamara & Lewell F. Gunter, 1989. "Off-Farm Earnings: the Impact of Economic Structure," The Review of Regional Studies, Southern Regional Science Association, vol. 19(3), pages 37-45, Fall.

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