Forecasting Trending Time Series with Relative Growth Rate Models

Many annual time series in socioeconomic systems are steadily increasing functions of time. This paper deals with an empirical approach to analyzing and projecting such trending time series from models of relative growth rates or percent changes. A class of relative growth rate models is defined which includes the linear. exponential, modified exponential and logistic growth curves as special eases. Parameters are estimated for the most part by linear regression techniques since the, relative growth rates for this class of models are linear in the parameters. The ternd curve for each model is obtained by integration and approximate-confidence limits can be obtained for the forecasts of future series values.