Abstract
A probabilistic methodology within a dynamic framework is proposed for the study of moments of errors in growth forecasts resulting from data uncertainty. This methodology is applied to well-known evolutionary models of growth, namely exponential and logistic. Explicit expressions for moments of the stochastic variable are derived. The paper explores methods based on two-point distribution approach, second-moment analysis, and probability distribution of parameters. Of these, the two-point distribution is found to be computationally advantageous. An interesting feature emerging from the analysis is that the mean and relative fluctuations in the projected variable of interest are numerically not much different from the respective ones when the uncertainties in the growth parameters are characterized by Gaussian, uniform or two-point distribution. This, however, holds for forecasting periods which are short in comparison with the time-scale of the process under study.
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