Timber product markets are subject to large shocks deriving from natural disturbances and policy shifts. Statistical modeling of shocks is often done to assess their economic importance. In this article, I simulate the statistical power of univariate and bivariate methods of shock detection using time series intervention models. Simulations show that bivariate methods are several times more statistically powerful than univariate methods when underlying series are nonstationary and potentially involved in cointegrating relationships. In an empirical application to detect the long-run price impacts of the voluntary phase-out of chromated copper arsenate in pressure-treating southern pine lumber for residential applications, I find the multivariate methods to be more powerful as well. I identify highly significant long-run price increases of 11% for two of three treated southern pine dimension lumber price series evaluated using multivariate approaches. The univariate method detected a long-run increase only for the third product, and the statistical significance was weak, although comparable, in magnitude to the first two products.
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.