Lee TW
We present a new method for automatically generating mathematical models of complex, non-linear processes, and apply it for tracking and predicting the Canadian GDP. This method is derived from solving complex, non-linear problems in engineering, and is found to be an efficient method for forecasting of financial and economic variables. The method involves setting up a general non-linear series involving terms of up to 3rd-order products, where the model coefficients are systematically determined by the data on the Canadian GDP along with significant economic indicators such as currency, gross demand deposits, consumer price index and various loan rates. Results show that GDP can be predicted quantitatively and qualitatively, at various prediction intervals, with longer-term predictions showing less agreement owing to divergent dynamics in the economic variables and GDP. Other complex financial and economic processes may be analyzed and predicted using this method.
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