Non-Linear Series Inversion Method for Forecasting Canadian GDP GrowthLee T.-W.*
School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ 85287-6106, USA
- *Corresponding Author:
- Lee TW
School for Engineering of Matter Transport
and Energy, Arizona State University
Tempe, AZ 85287-6106, USA
Tel: (480)965- 7989
E-mail: [email protected]
Received Date: June 23, 2015 Accepted Date: July 01, 2015 Published Date: July 08, 2015
Citation: Lee TW (2015) Non-Linear Series Inversion Method for Forecasting Canadian GDP Growth. Bus Eco J 6:165. doi:10.4172/2151-6219.1000165
Copyright: © 2015 Lee TW. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.