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A formula is presented for estimating the sensitivity of an input-output model's predictions of sectoral total gross outputs to errors in its technical coefficients without recalculating the inverse matrix. When building a "mongrel" input-ouput model (i.e., one based partly on primary data and partly on secondary data), the formula can be used to identify "significant" technical coefficients (those meriting estimation with primary data). Data from models of Humboldt County, California, and the state of Alabama are used to illustrate the use of the formula. Given plausible, albeit arbitrary, significance thresholds for errors in these models, relatively few technical coefficients in either appeared to merit estimation with primary data--indicating that the incremental costs associated with enhancing purely secondary data models may be quite low relative to the resulting gains in model reliability. For. Sci. 35(1):pp. 237-244.
Assistant Professor, Department of Forestry and Resource Management, University of California, Berkeley
Publication date: March 1, 1989
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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.