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Comparative Analysis of Stochastic Models for Financial Uncertainty in Forest Management

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We propose 13 continuous time stochastic models based on the state-dependent volatility process for stochastic log price dynamics, and then conduct a comparative analysis for their performance. Comparison is carried out on the basis of AIC (Akaike's Information Criterion), the mean squared error of the model, and the likelihood ratio test. Parameter estimation is performed by using the local linearization method. Our experiments with 13 tree species sold on the Japanese timber market show that the general form of the state-dependent stochastic model yields the highest performance in terms of AIC for most logs. In addition, we find that price dynamics with a tendency to increase over time can be captured by such a stochastic model, with the drift term as a linear function of the state. It is also shown that a functional form of the diffusion part or volatility of the model plays an important role in model performance with regard to the AIC, while a functional form of the drift part of the model does so in the mean squared error for forecasting one period ahead. FOR. SCI. 48(4):755–766.
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Keywords: Forest management; environmental management; forest; forest economics; forest management; forest resources; forestry; forestry research; forestry science; natural resource management; natural resources; price uncertainty; stochastic calculus; stochastic model

Document Type: Miscellaneous

Affiliations: 1: Department of Statistical Methodology, The Institute of Statistical Mathematics, Tokyo, Japan, 106-8569, Phone: +81-3-5421-8742; Fax: +81-3-542-8796 [email protected] 2: Institute of Policy & Planning Sciences, University of Tsukuba, Ibaraki, Japan, 305-8573,

Publication date: 2002-11-01

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    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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