To abstract the fundamental market structures of stumpage sold from the Siuslaw National Forest, a three-equation structural model with different assumptions on private cut was empirically derived. Under the private cut price-fixed assumption, the structural model can be simplified as a single-equation model with stumpage price as dependent variable and ordinary least squares (OLS) methods can be employed. If private cut is price responsive, two-stage least square (2SLS) methods must be applied. In both models, annual stumpage price can be predicted and forecasted into the future. With a private cut price responsive assumption, the stumpage price, private cut and total stumpage traded within the marketing area were determined simultaneously by systems of equations. For both models, classical negatively-sloping demand curves were obtained with stumpage price as the dependent variable. Also, final product prices and lumber production costs were found to be significant factors in the demand for stumpage. On the supply side for private cut, classical positively-sloping supply curves were obtained with private cut volume as a dependent variable, and growing stock inventory as an important factor that influences the private cut. The estimates of the short-run price elasticities of stumpage demand and private supply within the specified marketing area were both highly inelastic, while the demand for Siuslaw timber was highly elastic. It is felt that the overall results of the analysis compelled tentative acceptance of the hypothesis that the economic models for the stumpage market, developed in this thesis, are generally consistent with the true market structure of stumpage demand and supply and are useful for economic analysis and forecasting. Finally, the effects of alternative schedules for timber harvest from the Siuslaw National Forest on stumpage price, private cut, and total stumpage traded are examined. The estimated model is used to project market behavior under three alternative harvesting schedules on national forests for the period 1977 to 2030.