Identification of product life cycle models by autoregression–moving average models and Groebner’s bases
The authors offer the analytical models of product life cycle and the approach towards their classification based on the models of autoregression–moving average and using the Groebner bases for solving the normal systems of non-linear polynomial equations, received after using the least-squares method. The characteristics of modeling and forecasting fidelity have been also elaborated, concerning the sales data for cars, data for oil production, as well as interest of Google users towards cell phone models and guide-books edition.
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- Beveridge, Stephen & Nelson, Charles R., 1981. "A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the `business cycle'," Journal of Monetary Economics, Elsevier, vol. 7(2), pages 151-174.
- Diebold, Francis X & Mariano, Roberto S, 1995.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 13(3), pages 253-63, July.
- Tom Doan, . "DMARIANO: RATS procedure to compute Diebold-Mariano Forecast Comparison Test," Statistical Software Components RTS00055, Boston College Department of Economics.
- Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
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