An Urban Allocation Model Combining Time Series and Analytic Hierarchical Methods

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Abstract
Presented is a new application in urban economic forecasting. This method actively utilizes external information for household population forecasts. The purpose is to develop a more satisfying procedure as part of the management decision taking system. In the Portland Standard Metropolitan Statistical Area (SMSA), both time series and cross-sectional data are exploited. A transfer function-noise model is developed relating national output to SMSA employment. Household population for the SMSA is forecast utilizing the same statistical technique via a job opportunities/migration hypothesis. Forecasts are allocated to planning divisions within the SMSA by univariate stochastic models. However, these forecasts are adjusted after consideration of several land use indicators. The forecast standard errors are utilized and a hierarchical weighting scheme of the land-use indicators is developed within an allocation framework. Qualitative and quantitative information is merged to provide a more complete analysis and efficient estimates of the allocation weights.

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