- Title
- Good's casualty for time series: a regime-switching framework
- Creator
- Mlambo, Farai Fredric
- Subject
- Time-series analysis
- Subject
- Econometrics
- Date Issued
- 2014
- Date
- 2014
- Type
- Thesis
- Type
- Masters
- Type
- MCom
- Identifier
- http://hdl.handle.net/10948/6018
- Identifier
- vital:21025
- Description
- Causal analysis is a significant role-playing field in the applied sciences such as statistics, econometrics, and technometrics. Particularly, probability-raising models have warranted significant research interest. Most of the discussions in this area are philosophical in nature. Contemporarily, the econometric causality theory, developed by C.J.W. Granger, is popular in practical, time series causal applications. While this type of causality technique has many strong features, it has serious limitations. The processes studied, in particular, should be stationary and causal relationships are restricted to be linear. However, we cannot classify regime-switching processes as linear and stationary. I.J. Good proposed a probabilistic, event-type explication of causality that circumvents some of the limitations of Granger’s methodology. This work uses the probability raising causality ideology, as postulated by Good, to propose some causal analysis methodology applicable in a stochastic, non-stationary domain. There is a proposal made for a Good’s causality test, by transforming the originally specified probabilistic causality theory from random events to a stochastic, regime-switching framework. The researcher performed methodological validation via causality simulations for a Markov, regime-switching model. The proposed test can be used to detect whether none stochastic process is causal to the observed behaviour of another, probabilistically. In particular, the regime-switch causality explication proposed herein is pivotal to the results articulated. This research also examines the power of the proposed test by using simulations, and outlines some steps that one may take in using the test in a practical setting.
- Format
- vii, 75, 12 leaves
- Format
- Publisher
- Nelson Mandela Metropolitan University
- Publisher
- Faculty of Science
- Language
- English
- Rights
- Nelson Mandela Metropolitan University
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Collections
NMMU School of Computer Science, Mathematics, Physics and Statistics
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