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Nonparametric Tests for Profit Maximization with Incomplete Data: Application to the Pulp and Fine Paper Production in Finland

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Forest sector models give relevant information to decision makers only if they are based on correct economic hypotheses. This requires that the data from which the model parameters are estimated are consistent with the a priori assumptions made on market agents' behavior and competition patterns. For static models, rather simple nonparametric tests exist for checking such consistency. Such tests also provide an analyst with insights on possible errors in the data and the suitability of the data for the particular econometric model that the analyst has in mind. This article proposes data tests for the competitive or noncompetitive profit-maximization hypothesis that can also be used with incomplete data. The tests are applied to quarterly data on the production of pulp and fine paper in Finland. It is found that the data may not represent profit-maximizing price takers. Although this can be due to, e.g., integrated production, missing dynamics, or noncompetitive behavior—a hypothesis that was indeed found to be consistent with the fine paper data—it is evident that the modeler should not use these data in estimating a parametric industry model that builds on price-taking profit-maximizing behavior. The results emphasize the importance of testing the data before modeling. FOR. SCI. 51(1):19–28.
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Keywords: Cournot oligopoly; Forest products; environmental management; forest; forest industries; forest management; forest resources; forestry; forestry research; forestry science; market competition; monopoly; natural resource management; natural resources

Document Type: Regular Article

Affiliations: 1: Finnish Forest Research Institute Unioninkatu 40 A Helsinki Finland 00170 Phone: +358-10 211 2127;, Fax: +358-10 211 22101, Email: [email protected] 2: Finnish Forest Research Institute Unioninkatu 40 A Helsinki Finland 00170 Phone: +358-10 211 2232, Email: [email protected]

Publication date: 2005-02-01

More about this publication?
  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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