This paper uses historical socioeconomic data to evaluate the elasticities of a Dendrinos-Sonis one-population/two-locations nonlinear dynamic comparative advantage model for determining subregional shares of aggregate regional forecasts. The fractal dimension properties of the discrepancies between the actual and simulated data are used to enhance the forecasting framework. The analysis focuses in the first phase on total population, total personal income, and earnings by sector for the Columbus, Ohio Metropolitan Statistical Area, and Delaware County, one of its component counties. For the second phase of the analysis, these data for nondurable goods and for services are used, along with monthly data for total employment in Columbus, Cincinnati, and Cleveland, Ohio. Annual data for the analysis are drawn from the U. S. Bureau of Economic Analysis, Regional Economic Information System, while the monthly data come from the Ohio Bureau of Employment Securities. The nonlinear dynamic model is shown to outperform conventional approaches for the majority of socioeconomic stocks.
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