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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:English
English
Published: 2016
Subjects:
QA Mathematics
Online Access:http://etd.uum.edu.my/6060/
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spelling my.uum.etd.60602021-04-19T03:02:57Z http://etd.uum.edu.my/6060/ SURE-Autometrics algorithm for model selection in multiple equations Norhayati, Yusof 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 NonPeerReviewed text en /6060/1/s92279_01.pdf text en /6060/2/s92279_02.pdf Norhayati, Yusof (2016) SURE-Autometrics algorithm for model selection in multiple equations. PhD. thesis, Universiti Utara Malaysia.
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Electronic Theses
url_provider http://etd.uum.edu.my/
language English
English
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
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
publishDate 2016
url http://etd.uum.edu.my/6060/
_version_ 1698699498704863232
score 13.211869

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