Date of Award

3-24-2016

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Operational Sciences

First Advisor

Darryl K. Ahner, PhD.

Abstract

Complex systems science is a relatively new discipline and has not been widely applied to the field of economics. Much of current economic theory relies on principles of constrained optimization and often fails to see economic variables as part of an interconnected network. While tools for forecasting economic indicators are based primarily on autoregressive techniques, these techniques are not always well-suited to predicting the future performance of highly volatile data sets such as the stock market. This research portrays the stock market as one component of a networked system of economic variables, with the federal funds rate acting as an exogenous influencing factor. Together these components form a complex adaptive system having nonlinear dynamics. The network is modeled using a system of differential equations, which are based on an expanded form of the logistic differential equation for populations with carrying capacities. An inverse problem is solved using the method of least squares, and the resulting coefficients are examined to determine the strength of relationships between the network components. The fitted model is then evaluated for adequacy and Euler’s Forward Method is employed to predict the long-run behavior of the network. With this as a baseline, the research investigates several hypothetical scenarios to determine how the system reacts to changes in interest rates. Contributions and implications of the model are addressed in the context of U.S. national defense.

AFIT Designator

AFIT-ENS-MS-16-M-120

DTIC Accession Number

AD1053995

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