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

The 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 pac...

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Main Author: Mohammad Nasir, Muhammad Azim
Format: Thesis
Language:English
Published: 2020
Subjects:
QA1 Mathematics (General)
Online Access:http://eprints.usm.my/52558/1/Pages%20from%20Final%20Thesis%20Muhammad%20Azim%20Mohammad%20Nasir.pdf
http://eprints.usm.my/52558/
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spelling my.usm.eprints.52558 http://eprints.usm.my/52558/ Outliers And Structural Breaks Detection In Autoregressive Model By Indicator Saturation Approach Mohammad Nasir, Muhammad Azim QA1 Mathematics (General) The 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. 2020-11 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/52558/1/Pages%20from%20Final%20Thesis%20Muhammad%20Azim%20Mohammad%20Nasir.pdf Mohammad Nasir, Muhammad Azim (2020) Outliers And Structural Breaks Detection In Autoregressive Model By Indicator Saturation Approach. Masters thesis, Universiti Sains Malaysia.
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic QA1 Mathematics (General)
spellingShingle QA1 Mathematics (General)
Mohammad Nasir, Muhammad Azim
Outliers And Structural Breaks Detection In Autoregressive Model By Indicator Saturation Approach
description The 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.
format Thesis
author Mohammad Nasir, Muhammad Azim
author_facet Mohammad Nasir, Muhammad Azim
author_sort Mohammad Nasir, Muhammad Azim
title Outliers And Structural Breaks Detection In Autoregressive Model By Indicator Saturation Approach
title_short Outliers And Structural Breaks Detection In Autoregressive Model By Indicator Saturation Approach
title_full Outliers And Structural Breaks Detection In Autoregressive Model By Indicator Saturation Approach
title_fullStr Outliers And Structural Breaks Detection In Autoregressive Model By Indicator Saturation Approach
title_full_unstemmed Outliers And Structural Breaks Detection In Autoregressive Model By Indicator Saturation Approach
title_sort outliers and structural breaks detection in autoregressive model by indicator saturation approach
publishDate 2020
url http://eprints.usm.my/52558/1/Pages%20from%20Final%20Thesis%20Muhammad%20Azim%20Mohammad%20Nasir.pdf
http://eprints.usm.my/52558/
_version_ 1734300764223832064
score 13.211869

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