Autoregressive distributed lag modeling for Malaysia Palm Oil Prices

Modelling food commodities prices has become the area of interest in financial time series. This study aims to model Malaysian average monthly prices of crude palm oil using dynamic regression approach. The sample period covers from January 2000 until December 2013. The model investigated is Autore...

Full description

Bibliographic Details
Main Author:	Abang Mohammad Hudzaifah, Bin Abang Shakawi
Format:	Thesis
Language:	English English
Published:	Faculty of Science, Universiti Teknologi Malaysia 2014
Subjects:	GE Environmental Sciences
Online Access:	http://ir.unimas.my/8049/ http://ir.unimas.my/8049/1/Autoregressive%20distributed%20lag%20modeling%20for%20Malaysia%20Palm%20Oil%20Prices%20%2824pgs%29.pdf http://ir.unimas.my/8049/2/Autoregressive%20distributed%20lag%20modeling%20for%20Malaysia%20Palm%20Oil%20Prices.pdf

id	unimas-8049
recordtype	eprints
spelling	unimas-80492015-09-10T02:10:23Z http://ir.unimas.my/8049/ Autoregressive distributed lag modeling for Malaysia Palm Oil Prices Abang Mohammad Hudzaifah, Bin Abang Shakawi GE Environmental Sciences Modelling food commodities prices has become the area of interest in financial time series. This study aims to model Malaysian average monthly prices of crude palm oil using dynamic regression approach. The sample period covers from January 2000 until December 2013. The model investigated is Autoregressive Distributed Lag (ARDL) model. The model uses multivariate analysis with monthly prices, productions, imports, exports and closing stocks of crude palm oil as the variables. The ARDL model is selected using Akaike Information Criteria (AIC) and Schwartz-Bayesian Criteria (SBC). The capabilities of this model in estimating the crude palm oil prices is compared to Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) model by using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The process of modelling is done by using Eviews and Microfit statistical software. This study concluded that ARDL model is a better model in modelling the palm oil prices. The ARDL model selected by using AIC produce better estimation than the ARDL model selected by using SBC. Furthermore, there exist long-run relationship between crude palm oil prices and its determinants. Faculty of Science, Universiti Teknologi Malaysia 2014-06-12 Thesis NonPeerReviewed text en http://ir.unimas.my/8049/1/Autoregressive%20distributed%20lag%20modeling%20for%20Malaysia%20Palm%20Oil%20Prices%20%2824pgs%29.pdf text en http://ir.unimas.my/8049/2/Autoregressive%20distributed%20lag%20modeling%20for%20Malaysia%20Palm%20Oil%20Prices.pdf Abang Mohammad Hudzaifah, Bin Abang Shakawi (2014) Autoregressive distributed lag modeling for Malaysia Palm Oil Prices. Masters thesis, Universiti Teknologi Malaysia.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Sarawak
building	UNIMAS Institutional Repository
collection	Online Access
language	English English
topic	GE Environmental Sciences
spellingShingle	GE Environmental Sciences Abang Mohammad Hudzaifah, Bin Abang Shakawi Autoregressive distributed lag modeling for Malaysia Palm Oil Prices
description	Modelling food commodities prices has become the area of interest in financial time series. This study aims to model Malaysian average monthly prices of crude palm oil using dynamic regression approach. The sample period covers from January 2000 until December 2013. The model investigated is Autoregressive Distributed Lag (ARDL) model. The model uses multivariate analysis with monthly prices, productions, imports, exports and closing stocks of crude palm oil as the variables. The ARDL model is selected using Akaike Information Criteria (AIC) and Schwartz-Bayesian Criteria (SBC). The capabilities of this model in estimating the crude palm oil prices is compared to Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) model by using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The process of modelling is done by using Eviews and Microfit statistical software. This study concluded that ARDL model is a better model in modelling the palm oil prices. The ARDL model selected by using AIC produce better estimation than the ARDL model selected by using SBC. Furthermore, there exist long-run relationship between crude palm oil prices and its determinants.
format	Thesis
author	Abang Mohammad Hudzaifah, Bin Abang Shakawi
author_facet	Abang Mohammad Hudzaifah, Bin Abang Shakawi
author_sort	Abang Mohammad Hudzaifah, Bin Abang Shakawi
title	Autoregressive distributed lag modeling for Malaysia Palm Oil Prices
title_short	Autoregressive distributed lag modeling for Malaysia Palm Oil Prices
title_full	Autoregressive distributed lag modeling for Malaysia Palm Oil Prices
title_fullStr	Autoregressive distributed lag modeling for Malaysia Palm Oil Prices
title_full_unstemmed	Autoregressive distributed lag modeling for Malaysia Palm Oil Prices
title_sort	autoregressive distributed lag modeling for malaysia palm oil prices
publisher	Faculty of Science, Universiti Teknologi Malaysia
publishDate	2014
url	http://ir.unimas.my/8049/ http://ir.unimas.my/8049/1/Autoregressive%20distributed%20lag%20modeling%20for%20Malaysia%20Palm%20Oil%20Prices%20%2824pgs%29.pdf http://ir.unimas.my/8049/2/Autoregressive%20distributed%20lag%20modeling%20for%20Malaysia%20Palm%20Oil%20Prices.pdf
first_indexed	2018-09-06T15:31:11Z
last_indexed	2018-09-06T15:31:11Z
_version_	1610872648613494784

Autoregressive distributed lag modeling for Malaysia Palm Oil Prices

Similar Items