The potentialities of Chinese airline market for Lisbon international airport: the empirical modelling analysis
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10174/4983 |
Resumo: | In this paper we propose a comparative analysis between an empirical gravity model and a dynamic regression model with the objective to explain the potentialities of the Chinese airline market for Lisbon International Airport (air passengers demand). We confirm the viability to create some direct flights from Lisbon International Airport to Chinese airline market, with the strategy to attract the transfer passengers flow with origin on Latin America. Panel dada is used to determine the influence of the explanatory variables, on average number of passengers (air passengers demand). The results are creating by the Stata final outputs. We also demonstrate that the dynamic regression model used in this paper is more robust and better than the empirical gravity model, often considered as a reference method in the field of aviation. The most relevant variables on the dynamic regression model are PPP (gross national income (GNI) converted to international dollars using purchasing power parity rates), Business and Trade Factor, and Tourism and Cultural Factor. Furthermore, we find some possible explanations for the results. |
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The potentialities of Chinese airline market for Lisbon international airport: the empirical modelling analysisAir passengers demandChinese airline marketgravity modemodellingpanel datastataIn this paper we propose a comparative analysis between an empirical gravity model and a dynamic regression model with the objective to explain the potentialities of the Chinese airline market for Lisbon International Airport (air passengers demand). We confirm the viability to create some direct flights from Lisbon International Airport to Chinese airline market, with the strategy to attract the transfer passengers flow with origin on Latin America. Panel dada is used to determine the influence of the explanatory variables, on average number of passengers (air passengers demand). The results are creating by the Stata final outputs. We also demonstrate that the dynamic regression model used in this paper is more robust and better than the empirical gravity model, often considered as a reference method in the field of aviation. The most relevant variables on the dynamic regression model are PPP (gross national income (GNI) converted to international dollars using purchasing power parity rates), Business and Trade Factor, and Tourism and Cultural Factor. Furthermore, we find some possible explanations for the results.Senate House, University of London,2012-02-03T21:57:40Z2012-02-032011-12-18T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://hdl.handle.net/10174/4983http://hdl.handle.net/10174/4983enghttp://www.cfe-csda.org/ercim11/simnaonaovicentemba@gmail.comandreia@uevora.ptmmo@uevora.pt336Vicente, JoséDionisio, AndreiaOliveira, Manuelainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-03T18:43:17Zoai:dspace.uevora.pt:10174/4983Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:00:03.653096Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
The potentialities of Chinese airline market for Lisbon international airport: the empirical modelling analysis |
title |
The potentialities of Chinese airline market for Lisbon international airport: the empirical modelling analysis |
spellingShingle |
The potentialities of Chinese airline market for Lisbon international airport: the empirical modelling analysis Vicente, José Air passengers demand Chinese airline market gravity mode modelling panel data stata |
title_short |
The potentialities of Chinese airline market for Lisbon international airport: the empirical modelling analysis |
title_full |
The potentialities of Chinese airline market for Lisbon international airport: the empirical modelling analysis |
title_fullStr |
The potentialities of Chinese airline market for Lisbon international airport: the empirical modelling analysis |
title_full_unstemmed |
The potentialities of Chinese airline market for Lisbon international airport: the empirical modelling analysis |
title_sort |
The potentialities of Chinese airline market for Lisbon international airport: the empirical modelling analysis |
author |
Vicente, José |
author_facet |
Vicente, José Dionisio, Andreia Oliveira, Manuela |
author_role |
author |
author2 |
Dionisio, Andreia Oliveira, Manuela |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Vicente, José Dionisio, Andreia Oliveira, Manuela |
dc.subject.por.fl_str_mv |
Air passengers demand Chinese airline market gravity mode modelling panel data stata |
topic |
Air passengers demand Chinese airline market gravity mode modelling panel data stata |
description |
In this paper we propose a comparative analysis between an empirical gravity model and a dynamic regression model with the objective to explain the potentialities of the Chinese airline market for Lisbon International Airport (air passengers demand). We confirm the viability to create some direct flights from Lisbon International Airport to Chinese airline market, with the strategy to attract the transfer passengers flow with origin on Latin America. Panel dada is used to determine the influence of the explanatory variables, on average number of passengers (air passengers demand). The results are creating by the Stata final outputs. We also demonstrate that the dynamic regression model used in this paper is more robust and better than the empirical gravity model, often considered as a reference method in the field of aviation. The most relevant variables on the dynamic regression model are PPP (gross national income (GNI) converted to international dollars using purchasing power parity rates), Business and Trade Factor, and Tourism and Cultural Factor. Furthermore, we find some possible explanations for the results. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-12-18T00:00:00Z 2012-02-03T21:57:40Z 2012-02-03 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10174/4983 http://hdl.handle.net/10174/4983 |
url |
http://hdl.handle.net/10174/4983 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.cfe-csda.org/ercim11/ sim nao nao vicentemba@gmail.com andreia@uevora.pt mmo@uevora.pt 336 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Senate House, University of London, |
publisher.none.fl_str_mv |
Senate House, University of London, |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799136483100065792 |