Estimation for generalized linear cointegration regression models through composite quantile regression approach

Gespeichert in:

Autor*in:	Liu, Bingqi [verfasserIn] Pang, Tianxiao [verfasserIn] Cheng, Siang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Composite quantile regression Fully modified procedure Generalized linear cointegration regression model Portfolio optimization

Übergeordnetes Werk:	Enthalten in: Finance research letters - New York : Elsevier Science, 2004, 65(2024) vom: Juli, Artikel-ID 105567, Seite 1-14
Übergeordnetes Werk:	volume:65 ; year:2024 ; month:07 ; elocationid:105567 ; pages:1-14

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.1016/j.frl.2024.105567

Katalog-ID:	1893067122

Internformat


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912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
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982			\|2 26 \|1 00 \|x DE-206 \|b This paper introduces a meaningful approach employing composite quantile regression (CQR) to estimate generalized linear cointegration regression models. We elucidate the fundamental structure of the proposed model by presenting its underlying expressions and derive the asymptotic distribution of the estimates of model parameters. Through extensive simulations, our findings demonstrate the superior robustness and precision of the CQR method compared to ordinary least squares (OLS) and quantile regression (QR) approaches. The application of the model to economic and financial variables highlights its significant academic and practical value.

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publishDate	2024
allfields	10.1016/j.frl.2024.105567 doi (DE-627)1893067122 (DE-599)KXP1893067122 DE-627 ger DE-627 rda eng Liu, Bingqi verfasserin (DE-588)1341365921 (DE-627)1902133773 aut Estimation for generalized linear cointegration regression models through composite quantile regression approach Bingqi Liu, Tianxiao Pang, Siang Cheng 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Composite quantile regression (dpeaa)DE-206 Fully modified procedure (dpeaa)DE-206 Generalized linear cointegration regression model (dpeaa)DE-206 Portfolio optimization (dpeaa)DE-206 Pang, Tianxiao verfasserin (DE-588)1175540218 (DE-627)1046551310 (DE-576)516304356 aut Cheng, Siang verfasserin (DE-588)1341366111 (DE-627)1902133811 aut Enthalten in Finance research letters New York : Elsevier Science, 2004 65(2024) vom: Juli, Artikel-ID 105567, Seite 1-14 Online-Ressource (DE-627)387481583 (DE-600)2145766-9 (DE-576)259272752 1544-6123 nnns volume:65 year:2024 month:07 elocationid:105567 pages:1-14 https://www.sciencedirect.com/science/article/pii/S154461232400597X/pdfft?md5=bb77371e1f36dd9518694c97fd25daee&pid=1-s2.0-S154461232400597X-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.frl.2024.105567 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 65 2024 7 105567 1-14 26 01 0206 4544830753 x1z 02-07-24 26 00 DE-206 This paper introduces a meaningful approach employing composite quantile regression (CQR) to estimate generalized linear cointegration regression models. We elucidate the fundamental structure of the proposed model by presenting its underlying expressions and derive the asymptotic distribution of the estimates of model parameters. Through extensive simulations, our findings demonstrate the superior robustness and precision of the CQR method compared to ordinary least squares (OLS) and quantile regression (QR) approaches. The application of the model to economic and financial variables highlights its significant academic and practical value.
spelling	10.1016/j.frl.2024.105567 doi (DE-627)1893067122 (DE-599)KXP1893067122 DE-627 ger DE-627 rda eng Liu, Bingqi verfasserin (DE-588)1341365921 (DE-627)1902133773 aut Estimation for generalized linear cointegration regression models through composite quantile regression approach Bingqi Liu, Tianxiao Pang, Siang Cheng 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Composite quantile regression (dpeaa)DE-206 Fully modified procedure (dpeaa)DE-206 Generalized linear cointegration regression model (dpeaa)DE-206 Portfolio optimization (dpeaa)DE-206 Pang, Tianxiao verfasserin (DE-588)1175540218 (DE-627)1046551310 (DE-576)516304356 aut Cheng, Siang verfasserin (DE-588)1341366111 (DE-627)1902133811 aut Enthalten in Finance research letters New York : Elsevier Science, 2004 65(2024) vom: Juli, Artikel-ID 105567, Seite 1-14 Online-Ressource (DE-627)387481583 (DE-600)2145766-9 (DE-576)259272752 1544-6123 nnns volume:65 year:2024 month:07 elocationid:105567 pages:1-14 https://www.sciencedirect.com/science/article/pii/S154461232400597X/pdfft?md5=bb77371e1f36dd9518694c97fd25daee&pid=1-s2.0-S154461232400597X-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.frl.2024.105567 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 65 2024 7 105567 1-14 26 01 0206 4544830753 x1z 02-07-24 26 00 DE-206 This paper introduces a meaningful approach employing composite quantile regression (CQR) to estimate generalized linear cointegration regression models. We elucidate the fundamental structure of the proposed model by presenting its underlying expressions and derive the asymptotic distribution of the estimates of model parameters. Through extensive simulations, our findings demonstrate the superior robustness and precision of the CQR method compared to ordinary least squares (OLS) and quantile regression (QR) approaches. The application of the model to economic and financial variables highlights its significant academic and practical value.
allfields_unstemmed	10.1016/j.frl.2024.105567 doi (DE-627)1893067122 (DE-599)KXP1893067122 DE-627 ger DE-627 rda eng Liu, Bingqi verfasserin (DE-588)1341365921 (DE-627)1902133773 aut Estimation for generalized linear cointegration regression models through composite quantile regression approach Bingqi Liu, Tianxiao Pang, Siang Cheng 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Composite quantile regression (dpeaa)DE-206 Fully modified procedure (dpeaa)DE-206 Generalized linear cointegration regression model (dpeaa)DE-206 Portfolio optimization (dpeaa)DE-206 Pang, Tianxiao verfasserin (DE-588)1175540218 (DE-627)1046551310 (DE-576)516304356 aut Cheng, Siang verfasserin (DE-588)1341366111 (DE-627)1902133811 aut Enthalten in Finance research letters New York : Elsevier Science, 2004 65(2024) vom: Juli, Artikel-ID 105567, Seite 1-14 Online-Ressource (DE-627)387481583 (DE-600)2145766-9 (DE-576)259272752 1544-6123 nnns volume:65 year:2024 month:07 elocationid:105567 pages:1-14 https://www.sciencedirect.com/science/article/pii/S154461232400597X/pdfft?md5=bb77371e1f36dd9518694c97fd25daee&pid=1-s2.0-S154461232400597X-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.frl.2024.105567 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 65 2024 7 105567 1-14 26 01 0206 4544830753 x1z 02-07-24 26 00 DE-206 This paper introduces a meaningful approach employing composite quantile regression (CQR) to estimate generalized linear cointegration regression models. We elucidate the fundamental structure of the proposed model by presenting its underlying expressions and derive the asymptotic distribution of the estimates of model parameters. Through extensive simulations, our findings demonstrate the superior robustness and precision of the CQR method compared to ordinary least squares (OLS) and quantile regression (QR) approaches. The application of the model to economic and financial variables highlights its significant academic and practical value.
allfieldsGer	10.1016/j.frl.2024.105567 doi (DE-627)1893067122 (DE-599)KXP1893067122 DE-627 ger DE-627 rda eng Liu, Bingqi verfasserin (DE-588)1341365921 (DE-627)1902133773 aut Estimation for generalized linear cointegration regression models through composite quantile regression approach Bingqi Liu, Tianxiao Pang, Siang Cheng 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Composite quantile regression (dpeaa)DE-206 Fully modified procedure (dpeaa)DE-206 Generalized linear cointegration regression model (dpeaa)DE-206 Portfolio optimization (dpeaa)DE-206 Pang, Tianxiao verfasserin (DE-588)1175540218 (DE-627)1046551310 (DE-576)516304356 aut Cheng, Siang verfasserin (DE-588)1341366111 (DE-627)1902133811 aut Enthalten in Finance research letters New York : Elsevier Science, 2004 65(2024) vom: Juli, Artikel-ID 105567, Seite 1-14 Online-Ressource (DE-627)387481583 (DE-600)2145766-9 (DE-576)259272752 1544-6123 nnns volume:65 year:2024 month:07 elocationid:105567 pages:1-14 https://www.sciencedirect.com/science/article/pii/S154461232400597X/pdfft?md5=bb77371e1f36dd9518694c97fd25daee&pid=1-s2.0-S154461232400597X-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.frl.2024.105567 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 65 2024 7 105567 1-14 26 01 0206 4544830753 x1z 02-07-24 26 00 DE-206 This paper introduces a meaningful approach employing composite quantile regression (CQR) to estimate generalized linear cointegration regression models. We elucidate the fundamental structure of the proposed model by presenting its underlying expressions and derive the asymptotic distribution of the estimates of model parameters. Through extensive simulations, our findings demonstrate the superior robustness and precision of the CQR method compared to ordinary least squares (OLS) and quantile regression (QR) approaches. The application of the model to economic and financial variables highlights its significant academic and practical value.
allfieldsSound	10.1016/j.frl.2024.105567 doi (DE-627)1893067122 (DE-599)KXP1893067122 DE-627 ger DE-627 rda eng Liu, Bingqi verfasserin (DE-588)1341365921 (DE-627)1902133773 aut Estimation for generalized linear cointegration regression models through composite quantile regression approach Bingqi Liu, Tianxiao Pang, Siang Cheng 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Composite quantile regression (dpeaa)DE-206 Fully modified procedure (dpeaa)DE-206 Generalized linear cointegration regression model (dpeaa)DE-206 Portfolio optimization (dpeaa)DE-206 Pang, Tianxiao verfasserin (DE-588)1175540218 (DE-627)1046551310 (DE-576)516304356 aut Cheng, Siang verfasserin (DE-588)1341366111 (DE-627)1902133811 aut Enthalten in Finance research letters New York : Elsevier Science, 2004 65(2024) vom: Juli, Artikel-ID 105567, Seite 1-14 Online-Ressource (DE-627)387481583 (DE-600)2145766-9 (DE-576)259272752 1544-6123 nnns volume:65 year:2024 month:07 elocationid:105567 pages:1-14 https://www.sciencedirect.com/science/article/pii/S154461232400597X/pdfft?md5=bb77371e1f36dd9518694c97fd25daee&pid=1-s2.0-S154461232400597X-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.frl.2024.105567 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 65 2024 7 105567 1-14 26 01 0206 4544830753 x1z 02-07-24 26 00 DE-206 This paper introduces a meaningful approach employing composite quantile regression (CQR) to estimate generalized linear cointegration regression models. We elucidate the fundamental structure of the proposed model by presenting its underlying expressions and derive the asymptotic distribution of the estimates of model parameters. Through extensive simulations, our findings demonstrate the superior robustness and precision of the CQR method compared to ordinary least squares (OLS) and quantile regression (QR) approaches. The application of the model to economic and financial variables highlights its significant academic and practical value.
language	English
source	Enthalten in Finance research letters 65(2024) vom: Juli, Artikel-ID 105567, Seite 1-14 volume:65 year:2024 month:07 elocationid:105567 pages:1-14
sourceStr	Enthalten in Finance research letters 65(2024) vom: Juli, Artikel-ID 105567, Seite 1-14 volume:65 year:2024 month:07 elocationid:105567 pages:1-14
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institution	findex.gbv.de
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topic_facet	Composite quantile regression Fully modified procedure Generalized linear cointegration regression model Portfolio optimization
sw_local_iln_str_mv	26: DE-206:
isfreeaccess_bool	false
container_title	Finance research letters
authorswithroles_txt_mv	Liu, Bingqi @@aut@@ Pang, Tianxiao @@aut@@ Cheng, Siang @@aut@@
publishDateDaySort_date	2024-07-01T00:00:00Z
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author	Liu, Bingqi
spellingShingle	Liu, Bingqi misc Composite quantile regression misc Fully modified procedure misc Generalized linear cointegration regression model misc Portfolio optimization Estimation for generalized linear cointegration regression models through composite quantile regression approach
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topic_title	26 00 DE-206 This paper introduces a meaningful approach employing composite quantile regression (CQR) to estimate generalized linear cointegration regression models. We elucidate the fundamental structure of the proposed model by presenting its underlying expressions and derive the asymptotic distribution of the estimates of model parameters. Through extensive simulations, our findings demonstrate the superior robustness and precision of the CQR method compared to ordinary least squares (OLS) and quantile regression (QR) approaches. The application of the model to economic and financial variables highlights its significant academic and practical value Estimation for generalized linear cointegration regression models through composite quantile regression approach Bingqi Liu, Tianxiao Pang, Siang Cheng Composite quantile regression (dpeaa)DE-206 Fully modified procedure (dpeaa)DE-206 Generalized linear cointegration regression model (dpeaa)DE-206 Portfolio optimization (dpeaa)DE-206
topic	misc Composite quantile regression misc Fully modified procedure misc Generalized linear cointegration regression model misc Portfolio optimization
topic_unstemmed	misc Composite quantile regression misc Fully modified procedure misc Generalized linear cointegration regression model misc Portfolio optimization
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title_short	Estimation for generalized linear cointegration regression models through composite quantile regression approach
url	https://www.sciencedirect.com/science/article/pii/S154461232400597X/pdfft?md5=bb77371e1f36dd9518694c97fd25daee&pid=1-s2.0-S154461232400597X-main.pdf https://doi.org/10.1016/j.frl.2024.105567
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Estimation for generalized linear cointegration regression models through composite quantile regression approach

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