Using Excel to Simulate and Visualize Conditional Heteroskedastic Models
American Journal of Theoretical and Applied Statistics
Volume 7, Issue 6, November 2018, Pages: 242-246
Received: Oct. 29, 2018;
Accepted: Nov. 21, 2018;
Published: Dec. 18, 2018
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William Henry Laverty, Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, Canada
Ivan William Kelly, Department of Educational Psychology & Special Education, University of Saskatchewan, Saskatoon, Canada
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Longitudinal data are available in many disciplines, and quite often the mechanism generating the data are changing over time. These changes must be accounted for when modelling the data and subsequently drawing conclusions from the data. The three statistical models described in this article (GARCH, HMM, ARHMM) are appropriate modelling data with such changes. These three models are generalizations of a random walk. In a random walk the random changes over time have a constant distribution. The three models illustrated account for changes in the distribution of the random displacements over time. Our purpose in the article is to illustrate these three models and their intricacies using Excel. We would also contend and encourage the application of these three models to the analysis of other continuous data in fields utilizing social and medical data.
Time Series Analysis, GARCH, Hidden Markov Models (HMM), Autoregressive Hidden Markov (ARHMM), Simulation, Excel
To cite this article
William Henry Laverty,
Ivan William Kelly,
Using Excel to Simulate and Visualize Conditional Heteroskedastic Models, American Journal of Theoretical and Applied Statistics.
Vol. 7, No. 6,
2018, pp. 242-246.
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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