Analysis of timber markets in the U.S. South
Hood, Harrison Barksdale
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Timber markets in the U.S. South can be analyzed and evaluated in different ways and at multiple levels. These markets can be analyzed at a broad level to determine how markets for the same good interact with one another, in which case market integration can be studied. Traditional cointegration tests have been used to perform these analyses; however, these tests can only reveal the presence of stable or average long-term relationships. To evaluate market dynamics over time, a time-varying smooth transition autoregressive (TV-STAR) model has been modified to examine pine stumpage markets throughout this region. The proposed model incorporates an economic indicator and allows us to evaluate market integration as it changes throughout a specified time period. Timber markets can also be evaluated to observe how different components of the timber sale process effect prices. The sale of timber contains a number of different components unique to each specific sale, and includes items such as timber price, location of sale, total volume harvested, and sale size. The second aspect of this study evaluates pine sawtimber stumpage prices by modeling monthly prices using a seasonal autoregressive integrated moving-average (SARIMA) model. Previous studies have examined how timber prices are influenced by different sale characteristics; however, such studies have been confined to smaller market areas. The focus of this research is to observe which specific characteristics influence stumpage prices on a much larger region-wide basis. Results indicate that three specific factors significantly influence expected price: sale size, sale type, and competing hardwood timber prices. Furthermore, we found evidence that stumpage prices have settled at a new level, where prices will presumably remain in the immediate future. Mill location and demand (capacity) are also know to influence market prices for different timber products, so the third aspect of this study sought to determine the degree to which these two variables effect stumpage prices for both pine sawtimber and pine pulpwood. Individual timber sales were converted into panel data for this analysis, and results indicate that a timber sale’s location relative to surrounding mills has a significant influence on expected stumpage price.