The Statistical Interpretation of Predictions with Disaggregate Demand Models

Carlos F. Daganzo
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Carlos F. Daganzo: University of California, Berkeley, California

Transportation Science, 1979, vol. 13, issue 1, 1-12

Abstract: This paper discusses an element of forecasting with disaggregate demand models that has received little attention so far; namely, the extent to which the accuracy of the final prediction depends on the accuracy of the calibration process. The paper introduces a numerical technique to evaluate approximate confidence intervals for the expected number of people using a transportation facility and approximate prediction intervals for the actual usage. It is shown that, unless the magnitude of the variance of the estimated parameters is considerably small, the predictions that result may be biased and the resulting confidence intervals, inaccurate. The degree of accuracy that can be obtained with different parameter variances is illustrated numerically for the binary probit model.

Date: 1979
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:13:y:1979:i:1:p:1-12

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