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This dissertation research endeavors to provide a better understanding of effects of confidence on economic output by assessing and quantifying the negative impacts of confidence loss on economy to assist policy makers in their decision making processes and in using economic stimulus. This study introduces an overarching model that captures a linkage between confidence -by using three confidence indices- and various economic sectors as well as the ripple effects of a loss of confidence on these industries by utilizing time-series analysis, input-output model and Monte Carlo simulation.In this study, first, a model developed to analyze the relationships between confidence indices and economic output by employing time-series analysis. The study, then, incorporates the time-series models into input-output analysis to determine the effects of confidence on various economic sectors. Next, scenario analysis is conducted by using Monte Carlo simulation to produce the probability of outcomes and to generate the ripple effects of certain defined confidence indicator scenarios. The study also quantifies "bull" and "bear" market conditions to inform policy makers on potential means of directing economic stimulus when confidence is low.It is important provide a better understanding of the role of confidence on the economy for policy makers to improve decision-making processes, including in providing and targeting economic stimulus. By describing a relationship between confidence indices and real GDP-by-industry, determining the amount of real economic loss associated with a certain percentage loss in confidence, assessing the ripple effects of a loss of confidence on individual economic sectors and quantifying "bull" and "bear" market conditions, this study aims to provide policy makers a better understanding of the role and significance of these indices, which can guide actions to inversely influence the indices through targeted economic stimulus with the goal, for example, to reverse a downward spiral trend.

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