SURE-Autometrics algorithm for model selection in multiple equations

The ambiguous process of model building can be explained by expert modellers due to their tacit knowledge acquired through research experiences. Meanwhile, practitioners who are usually non-experts and lack of statistical knowledge will face difficulties during the modelling process. Hence, algorit...

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Main Author:	Norhayati, Yusof
Format:	Thesis
Language:	eng eng
Published:	2016
Subjects:	QA Mathematics
Online Access:	https://etd.uum.edu.my/6060/1/s92279_01.pdf https://etd.uum.edu.my/6060/2/s92279_02.pdf
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id	my-uum-etd.6060
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Ismail, Suzilah
topic	QA Mathematics
spellingShingle	QA Mathematics Norhayati, Yusof SURE-Autometrics algorithm for model selection in multiple equations
description	The ambiguous process of model building can be explained by expert modellers due to their tacit knowledge acquired through research experiences. Meanwhile, practitioners who are usually non-experts and lack of statistical knowledge will face difficulties during the modelling process. Hence, algorithm with a step by step guidance is beneficial in model building, testing and selection. However, most model selection algorithms such as Autometrics only concentrate on single equation modelling which has limited application. Thus, this study aims to develop an algorithm for model selection in multiple equations focusing on seemingly unrelated regression equations (SURE) model. The algorithm is developed by integrating the SURE model with the Autometrics search strategy; hence, it is named as SURE-Autometrics. Its performance is assessed using Monte Carlo simulation experiments based on five specification models, three strengths of correlation disturbances and two sample sizes. Two sets of general unrestricted models (GUMS) are then formulated by adding a number of irrelevant variables to the specification models. The performance is measured by the percentages of SURE-Autometrics algorithm that are able to eliminate the irrelevant variables from the initial GUMS of two, four and six equations. The SURE-Autometrics is also validated using two sets of real data by comparing the forecast error measures with five model selection algorithms and three non-algorithm procedures. The findings from simulation experiments suggested that SURE-Autometrics performed well when the number of equations and number of relevant variables in the true specification model were minimal. Its application on real data indicated that several models are able to forecast accurately if the data has no quality problem. This automatic model selection algorithm is better than non-algorithm procedure which requires knowledge and extra time. In conclusion, the performance of model selection in multiple equations using SURE-Autometrics is dependent upon data quality and complexities of the SURE model.
format	Thesis
qualification_name	Ph.D.
qualification_level	Doctorate
author	Norhayati, Yusof
author_facet	Norhayati, Yusof
author_sort	Norhayati, Yusof
title	SURE-Autometrics algorithm for model selection in multiple equations
title_short	SURE-Autometrics algorithm for model selection in multiple equations
title_full	SURE-Autometrics algorithm for model selection in multiple equations
title_fullStr	SURE-Autometrics algorithm for model selection in multiple equations
title_full_unstemmed	SURE-Autometrics algorithm for model selection in multiple equations
title_sort	sure-autometrics algorithm for model selection in multiple equations
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2016
url	https://etd.uum.edu.my/6060/1/s92279_01.pdf https://etd.uum.edu.my/6060/2/s92279_02.pdf
_version_	1747828016821567488
spelling	my-uum-etd.60602021-04-19T03:02:57Z SURE-Autometrics algorithm for model selection in multiple equations 2016 Norhayati, Yusof Ismail, Suzilah Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA Mathematics The ambiguous process of model building can be explained by expert modellers due to their tacit knowledge acquired through research experiences. Meanwhile, practitioners who are usually non-experts and lack of statistical knowledge will face difficulties during the modelling process. Hence, algorithm with a step by step guidance is beneficial in model building, testing and selection. However, most model selection algorithms such as Autometrics only concentrate on single equation modelling which has limited application. Thus, this study aims to develop an algorithm for model selection in multiple equations focusing on seemingly unrelated regression equations (SURE) model. The algorithm is developed by integrating the SURE model with the Autometrics search strategy; hence, it is named as SURE-Autometrics. Its performance is assessed using Monte Carlo simulation experiments based on five specification models, three strengths of correlation disturbances and two sample sizes. Two sets of general unrestricted models (GUMS) are then formulated by adding a number of irrelevant variables to the specification models. The performance is measured by the percentages of SURE-Autometrics algorithm that are able to eliminate the irrelevant variables from the initial GUMS of two, four and six equations. The SURE-Autometrics is also validated using two sets of real data by comparing the forecast error measures with five model selection algorithms and three non-algorithm procedures. The findings from simulation experiments suggested that SURE-Autometrics performed well when the number of equations and number of relevant variables in the true specification model were minimal. Its application on real data indicated that several models are able to forecast accurately if the data has no quality problem. This automatic model selection algorithm is better than non-algorithm procedure which requires knowledge and extra time. In conclusion, the performance of model selection in multiple equations using SURE-Autometrics is dependent upon data quality and complexities of the SURE model. 2016 Thesis https://etd.uum.edu.my/6060/ https://etd.uum.edu.my/6060/1/s92279_01.pdf text eng public https://etd.uum.edu.my/6060/2/s92279_02.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia Armstrong, J. S., & Collopy, F. (1992). Error measures for generalising about forecasting methods: Empirical comparison. International Journal of Forecasting. Armstrong, J. S., & Fildes, R. 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SURE-Autometrics algorithm for model selection in multiple equations

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