Regression Quantiles under Heteroscedasticity and Multicollinearity: Analysis of Travel and Tourism Competitiveness

0501434 - ÚI 2020 RIV SK eng J - Článek v odborném periodiku
Kalina, Jan - Vašaničová, P. - Litavcová, E.
Regression Quantiles under Heteroscedasticity and Multicollinearity: Analysis of Travel and Tourism Competitiveness.
Ekonomický časopis. Roč. 67, č. 1 (2019), s. 69-85. ISSN 0013-3035. E-ISSN 0013-3035
Grant ostatní: GA ČR(CZ) GA17-07384S
Institucionální podpora: RVO:67985807
Klíčová slova: linear regression * model selection * robustness * regression quantiles * lasso * tourism
Obor OECD: Statistics and probability
Impakt faktor: 0.560, rok: 2019
Způsob publikování: Open access
https://www.sav.sk/journals/uploads/0125113201%2019%20Kalina%20+%20RS.pdf

In the linear regression, heteroscedasticity and multicollinearity can be characterized as intertwined problems, which often simultaneously appear in econometric models. The aim of this paper is to discuss various approaches to regression modelling for heteroscedastic multi collinear data. A real economic dataset from the World Economic Forum serves as an illustration of various individual methods and the paper provides a practical motivation for quantile regression and particularly for regularized regression quantiles. In the dataset, tourist service infrastructure across 141 countries is modelled as a response of 12 characteristics of the Travel and Tourism Competitiveness Index (TTCI). Regression quantiles and their lasso estimates turn out to be more suitable for the dataset compared to more traditional econometric tools.
Trvalý link: http://hdl.handle.net/11104/0293472