The Autoregressive Distributed Lag Extensions And Applications

The autoregressive distributed lag (ARDL) framework is comprehensive with both flexible lag orders of dependent and independent variables to explain the dynamic of the responding variable. This thesis extends the ARDL methods to provide practical applications in economic analysis. The extensions inc...

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Main Author:	Sam, Chung Yan
Format:	Thesis
Language:	English
Published:	2022
Subjects:	Q179.9-180 Research
Online Access:	http://eprints.usm.my/59126/1/SAM%20CHUNG%20YAN%20-%20TESIS%20cut.pdf
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id	my-usm-ep.59126
record_format	uketd_dc
spelling	my-usm-ep.591262023-08-15T01:51:37Z The Autoregressive Distributed Lag Extensions And Applications 2022-07 Sam, Chung Yan Q179.9-180 Research The autoregressive distributed lag (ARDL) framework is comprehensive with both flexible lag orders of dependent and independent variables to explain the dynamic of the responding variable. This thesis extends the ARDL methods to provide practical applications in economic analysis. The extensions include providing bounds of critical values of the additional testing on lagged level independent variables coefficients for the ARDL cointegration test, innovating the ARDL framework for a multivariate unit root test, and proposing ARDL model for Taylor rule studies. Users who are unfamiliar with programming could use the provided critical values to perform the familiar bounds testing procedure to run the cointegration test proposed by McNown et al. (2018). The dynamic and flexible model in the multivariate ARDL unit root test with related time series information helps to gain more statistical power than the univariate framework unit root tests. Besides that, the limitation in the covariate Dickey-Fuller framework that rules out the possibility of cointegration is covered by the ARDL model to avoid power loss caused by model misspecification. Many Taylor rule empirical studies do not follow proper econometric procedures such as unit root, cointegration, and diagnostic tests. Besides, many pieces of evidence show that the Taylor rule regression is unbalanced with mixed integration order variables. Therefore, level estimates without cointegration reported in the studies could give spurious results. The robust standard error is commonly used in empirical studies, but it is helpless to deal with the residual autocorrelation problem, especially if the model includes lagged dependent variable. 2022-07 Thesis http://eprints.usm.my/59126/ http://eprints.usm.my/59126/1/SAM%20CHUNG%20YAN%20-%20TESIS%20cut.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Penyelidikan Dasar & Kajian Antarabangsa
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	Q179.9-180 Research
spellingShingle	Q179.9-180 Research Sam, Chung Yan The Autoregressive Distributed Lag Extensions And Applications
description	The autoregressive distributed lag (ARDL) framework is comprehensive with both flexible lag orders of dependent and independent variables to explain the dynamic of the responding variable. This thesis extends the ARDL methods to provide practical applications in economic analysis. The extensions include providing bounds of critical values of the additional testing on lagged level independent variables coefficients for the ARDL cointegration test, innovating the ARDL framework for a multivariate unit root test, and proposing ARDL model for Taylor rule studies. Users who are unfamiliar with programming could use the provided critical values to perform the familiar bounds testing procedure to run the cointegration test proposed by McNown et al. (2018). The dynamic and flexible model in the multivariate ARDL unit root test with related time series information helps to gain more statistical power than the univariate framework unit root tests. Besides that, the limitation in the covariate Dickey-Fuller framework that rules out the possibility of cointegration is covered by the ARDL model to avoid power loss caused by model misspecification. Many Taylor rule empirical studies do not follow proper econometric procedures such as unit root, cointegration, and diagnostic tests. Besides, many pieces of evidence show that the Taylor rule regression is unbalanced with mixed integration order variables. Therefore, level estimates without cointegration reported in the studies could give spurious results. The robust standard error is commonly used in empirical studies, but it is helpless to deal with the residual autocorrelation problem, especially if the model includes lagged dependent variable.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Sam, Chung Yan
author_facet	Sam, Chung Yan
author_sort	Sam, Chung Yan
title	The Autoregressive Distributed Lag Extensions And Applications
title_short	The Autoregressive Distributed Lag Extensions And Applications
title_full	The Autoregressive Distributed Lag Extensions And Applications
title_fullStr	The Autoregressive Distributed Lag Extensions And Applications
title_full_unstemmed	The Autoregressive Distributed Lag Extensions And Applications
title_sort	autoregressive distributed lag extensions and applications
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Penyelidikan Dasar & Kajian Antarabangsa
publishDate	2022
url	http://eprints.usm.my/59126/1/SAM%20CHUNG%20YAN%20-%20TESIS%20cut.pdf
_version_	1776101251501522944

The Autoregressive Distributed Lag Extensions And Applications

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