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A robust vector autoregressive model for forecasting economic growth in Malaysia

Economic indicator measures how solid or strong an economy of a country is. Basically, economic growth can be measured by using the economic indicators as they give an account of the quality or shortcoming of an economy. Vector Auto-regressive (VAR) method is commonly useful in forecasting the econo...

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Main Authors: Khamis, Azme, Abdul Razak, Nur Azreen, Affendi Abdullah, Mohd Asrul
Format: Article
Language:English
Published: UTM Press 2018
Subjects:
HD72-88 Economic growth, development, planning
Online Access:http://eprints.uthm.edu.my/5911/1/AJ%202018%20%28649%29.pdf
http://eprints.uthm.edu.my/5911/
http://dx.doi.org/10.11113/mjfas.v14n3.1021
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spelling my.uthm.eprints.59112022-01-24T08:42:10Z http://eprints.uthm.edu.my/5911/ A robust vector autoregressive model for forecasting economic growth in Malaysia Khamis, Azme Abdul Razak, Nur Azreen Affendi Abdullah, Mohd Asrul HD72-88 Economic growth, development, planning Economic indicator measures how solid or strong an economy of a country is. Basically, economic growth can be measured by using the economic indicators as they give an account of the quality or shortcoming of an economy. Vector Auto-regressive (VAR) method is commonly useful in forecasting the economic growth involving a bounteous of economic indicators. However, problems arise when its parameters are estimated using least square method which is very sensitive to the outliers existence. Thus, the aim of this study is to propose the best method in dealing with the outliers data so that the forecasting result is not biased. Data used in this study are the economic indicators monthly basis starting from January 1998 to January 2016. Two methods are considered, which are filtering technique via least median square (LMS), least trimmed square (LTS), least quartile difference (LQD) and imputation technique via mean and median. Using the mean absolute percentage error (MAPE) as the forecasting performance measure, this study concludes that Robust VAR with LQD filtering is a more appropriate model compare to others model. UTM Press 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/5911/1/AJ%202018%20%28649%29.pdf Khamis, Azme and Abdul Razak, Nur Azreen and Affendi Abdullah, Mohd Asrul (2018) A robust vector autoregressive model for forecasting economic growth in Malaysia. Malaysian Journal of Fundamental and Applied Sciences, 14 (3). pp. 382-385. ISSN 2289-599X http://dx.doi.org/10.11113/mjfas.v14n3.1021
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic HD72-88 Economic growth, development, planning
spellingShingle HD72-88 Economic growth, development, planning
Khamis, Azme
Abdul Razak, Nur Azreen
Affendi Abdullah, Mohd Asrul
A robust vector autoregressive model for forecasting economic growth in Malaysia
description Economic indicator measures how solid or strong an economy of a country is. Basically, economic growth can be measured by using the economic indicators as they give an account of the quality or shortcoming of an economy. Vector Auto-regressive (VAR) method is commonly useful in forecasting the economic growth involving a bounteous of economic indicators. However, problems arise when its parameters are estimated using least square method which is very sensitive to the outliers existence. Thus, the aim of this study is to propose the best method in dealing with the outliers data so that the forecasting result is not biased. Data used in this study are the economic indicators monthly basis starting from January 1998 to January 2016. Two methods are considered, which are filtering technique via least median square (LMS), least trimmed square (LTS), least quartile difference (LQD) and imputation technique via mean and median. Using the mean absolute percentage error (MAPE) as the forecasting performance measure, this study concludes that Robust VAR with LQD filtering is a more appropriate model compare to others model.
format Article
author Khamis, Azme
Abdul Razak, Nur Azreen
Affendi Abdullah, Mohd Asrul
author_facet Khamis, Azme
Abdul Razak, Nur Azreen
Affendi Abdullah, Mohd Asrul
author_sort Khamis, Azme
title A robust vector autoregressive model for forecasting economic growth in Malaysia
title_short A robust vector autoregressive model for forecasting economic growth in Malaysia
title_full A robust vector autoregressive model for forecasting economic growth in Malaysia
title_fullStr A robust vector autoregressive model for forecasting economic growth in Malaysia
title_full_unstemmed A robust vector autoregressive model for forecasting economic growth in Malaysia
title_sort robust vector autoregressive model for forecasting economic growth in malaysia
publisher UTM Press
publishDate 2018
url http://eprints.uthm.edu.my/5911/1/AJ%202018%20%28649%29.pdf
http://eprints.uthm.edu.my/5911/
http://dx.doi.org/10.11113/mjfas.v14n3.1021
_version_ 1738581430109208576
score 13.211869

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