Outliers And Structural Breaks Detection In Autoregressive Model By Indicator Saturation Approach

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dc.contributor.authorMuhammad Azim Mohammad Nasir
dc.date.accessioned2022-05-23T07:04:09Z
dc.date.available2022-05-23T07:04:09Z
dc.date.issued2020-11
dc.description.abstractThe indicator saturation approach is one of the latest methods in the literature that Can detect both the outlier and structural break dates simultaneously in a financial time series data. As the approach applied general-to-specific modelling in identifying the most significant indicators, gets package in r and autometrics in oxmetrics can handle the concerns of more variables than observations number, t . As far as we are aware of, all the leading researches use autometrics in their research and most of them carried out simple static data generating process, (dgp) in monte carlo simulations to investigate the performance of indicator saturation.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/15272
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectOutliers And Structural Breaks Detection In Autoregressiveen_US
dc.subjectModel By Indicator Saturation Approachen_US
dc.titleOutliers And Structural Breaks Detection In Autoregressive Model By Indicator Saturation Approachen_US
dc.typeThesisen_US
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