Sequential Change Point Estimation Using Empirical Likelihood for Time Series Data

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dc.contributor.advisorAnanda Kubeen_US
dc.contributor.authorMachuka, Carolyne Kemunto
dc.date.accessioned2024-02-05T06:20:50Z
dc.date.available2024-02-05T06:20:50Z
dc.date.issued2023-09
dc.descriptionA Research Project Submitted In Partial Fulfillment of the Requirement for the Award of a Degree of Masters of Science (Statistics) in the School of Pure and Applied Sciences of Kenyatta University, September 2023.en_US
dc.description.abstractSequential Change point detection has enhanced the reliance on analysis of live data as it streams into the system to support real-time decision-making processes. This has played a key role in the advancement of time series modeling and forecasting in financial time series and risk management. This domain has a growing demand to identify change points precisely and efficiently for development of automated analytical models. In this work, sequential change point estimation based on empirical likelihood test statistic is developed by maximizing the likelihood of the empirical distribution of the data subject to constraints based on the sample moments. Change point is declared when the test statistic exceeds a set threshold. The threshold is set such that it maximises the power of rejecting the null hypothesis. A stop time function is defined based on the null hypothesis. Consistency of the change point estimator has been verified through monte carlo simulations based on different sample sizes to demonstrate the empirical power of the test statistic. As the pre change historical data grew unbounded, the bias of the Mean Absolute Error (MAE) approaches zero. The estimator converges closer to the true values. The estimator was fitted on KES/USD exchange rate data from January 2017 to December 2021.en_US
dc.description.sponsorshipKenyatta Universityen_US
dc.identifier.urihttps://ir-library.ku.ac.ke/handle/123456789/27490
dc.language.isoenen_US
dc.publisherKenyatta Universityen_US
dc.subjectSequential Change Point Estimationen_US
dc.subjectEmpirical Likelihooden_US
dc.subjectTime Series Dataen_US
dc.titleSequential Change Point Estimation Using Empirical Likelihood for Time Series Dataen_US
dc.typeThesisen_US
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