Forecasting Technological Substitutions with Concurrent Short Time Series

Abstract It has been shown in the literature that the data of technological substitutions exhibit a strong correlation across different time periods, that is, a strong serial correlation. Significant improvement in predicting such substitutions has been achieved by incorporating serial correlation and power transformation parameters into four growth curve models. The modified models, or data-based transformed models, however, break down when the number of time points is small. This article proposes a generalized growth curve model for forecasting technological substitutions with concurrent short time series. The model combines the concepts of power transformations and repeated measurements with a common serial covariance structure. Concurrent time series for several cases provide the repeated measurement requirement of the model. Improvement in forecasting accuracy by using this model is demonstrated with a set of telephone switching data.

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