Improvement of Vector Autoregression (VAR) estimation using Combine White Noise (CWN) technique

Improvement of Vector Autoregression (VAR) estimation using Combine White Noise (CWN) technique

Previous studies revealed that Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH) outperformed Vector Autoregression (VAR) when data exhibit heteroscedasticity. However, EGARCH estimation is not efficient when the data have leverage effect. Therefore, in this study the weakn...

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Main Author: Abraham, Agboluaje Ayodele
Format: Thesis
Language:eng
eng
eng
Published: 2018
Subjects:
QA71-90 Instruments and machines
T Technology (General)
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institution Universiti Utara Malaysia
collection UUM ETD
language eng
eng
eng
advisor Ismail, Suzilah
Yip, Chee Yin
topic QA71-90 Instruments and machines
T Technology (General)
spellingShingle QA71-90 Instruments and machines
T Technology (General)
Abraham, Agboluaje Ayodele
Improvement of Vector Autoregression (VAR) estimation using Combine White Noise (CWN) technique
description Previous studies revealed that Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH) outperformed Vector Autoregression (VAR) when data exhibit heteroscedasticity. However, EGARCH estimation is not efficient when the data have leverage effect. Therefore, in this study the weaknesses of VAR and EGARCH were modelled using Combine White Noise (CWN). The CWN model was developed by integrating the white noise of VAR with EGARCH using Bayesian Model Averaging (BMA) for the improvement of VAR estimation. First, the standardized residuals of EGARCH errors (heteroscedastic variance) were decomposed into equal variances and defined as white noise series. Next, this series was transformed into CWN model through BMA. The CWN was validated using comparison study based on simulation and four countries real data sets of Gross Domestic Product (GDP). The data were simulated by incorporating three sample sizes with low, moderate and high values of leverages and skewness. The CWN model was compared with three existing models (VAR, EGARCH and Moving Average (MA)). Standard error, log-likelihood, information criteria and forecast error measures were used to evaluate the performance of the models. The simulation findings showed that CWN outperformed the three models when using sample size of 200 with high leverage and moderate skewness. Similar results were obtained for the real data sets where CWN outperformed the three models with high leverage and moderate skewness using France GDP. The CWN also outperformed the three models when using the other three countries GDP data sets. The CWN was the most accurate model of about 70 percent as compared with VAR, EGARCH and MA models. These simulated and real data findings indicate that CWN are more accurate and provide better alternative to model heteroscedastic data with leverage effect.
format Thesis
qualification_name Ph.D.
qualification_level Doctorate
author Abraham, Agboluaje Ayodele
author_facet Abraham, Agboluaje Ayodele
author_sort Abraham, Agboluaje Ayodele
title Improvement of Vector Autoregression (VAR) estimation using Combine White Noise (CWN) technique
title_short Improvement of Vector Autoregression (VAR) estimation using Combine White Noise (CWN) technique
title_full Improvement of Vector Autoregression (VAR) estimation using Combine White Noise (CWN) technique
title_fullStr Improvement of Vector Autoregression (VAR) estimation using Combine White Noise (CWN) technique
title_full_unstemmed Improvement of Vector Autoregression (VAR) estimation using Combine White Noise (CWN) technique
title_sort improvement of vector autoregression (var) estimation using combine white noise (cwn) technique
granting_institution Universiti Utara Malaysia
granting_department Awang Had Salleh Graduate School of Arts & Sciences
publishDate 2018
url https://etd.uum.edu.my/6900/1/DepositPermission_s94907.pdf
https://etd.uum.edu.my/6900/2/s94907_01.pdf
https://etd.uum.edu.my/6900/3/s94907_02.pdf
_version_ 1747828125466624000
spelling my-uum-etd.69002021-08-09T02:16:20Z Improvement of Vector Autoregression (VAR) estimation using Combine White Noise (CWN) technique 2018 Abraham, Agboluaje Ayodele Ismail, Suzilah Yip, Chee Yin Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA71-90 Instruments and machines T Technology (General) Previous studies revealed that Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH) outperformed Vector Autoregression (VAR) when data exhibit heteroscedasticity. However, EGARCH estimation is not efficient when the data have leverage effect. Therefore, in this study the weaknesses of VAR and EGARCH were modelled using Combine White Noise (CWN). The CWN model was developed by integrating the white noise of VAR with EGARCH using Bayesian Model Averaging (BMA) for the improvement of VAR estimation. First, the standardized residuals of EGARCH errors (heteroscedastic variance) were decomposed into equal variances and defined as white noise series. Next, this series was transformed into CWN model through BMA. The CWN was validated using comparison study based on simulation and four countries real data sets of Gross Domestic Product (GDP). The data were simulated by incorporating three sample sizes with low, moderate and high values of leverages and skewness. The CWN model was compared with three existing models (VAR, EGARCH and Moving Average (MA)). Standard error, log-likelihood, information criteria and forecast error measures were used to evaluate the performance of the models. The simulation findings showed that CWN outperformed the three models when using sample size of 200 with high leverage and moderate skewness. Similar results were obtained for the real data sets where CWN outperformed the three models with high leverage and moderate skewness using France GDP. The CWN also outperformed the three models when using the other three countries GDP data sets. The CWN was the most accurate model of about 70 percent as compared with VAR, EGARCH and MA models. These simulated and real data findings indicate that CWN are more accurate and provide better alternative to model heteroscedastic data with leverage effect. 2018 Thesis https://etd.uum.edu.my/6900/ https://etd.uum.edu.my/6900/1/DepositPermission_s94907.pdf text eng public https://etd.uum.edu.my/6900/2/s94907_01.pdf text eng public https://etd.uum.edu.my/6900/3/s94907_02.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia Ahmed, M., Aslam, M., & Pasha, G. R. (2011). Inference under heteroscedasticity of unknown form using an adaptive estimator. Communications in Statistics-Theory and Methods, 40(24), 4431–4457. doi.org/10.1080/03610926.2010.513793 Almeida, D. D., & Hotta, L. K. (2014). 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