0583644 20240402215305.920240305235959.9 10.32725/978-80-7694-053-6.63 6 s. P cav_un_epca*0583640978-80-7694-053-6Proceedings of the 17th International Scientific Conference INPROFORUM: Challenges and Opportunities in the Digital World5-10České BudějoviceUniversity of South Bohemia in České Budějovice, Faculty of Economics2023RolínekL. linear regression assumptions non-standard situations robustness diagnostics cav_un_auth*0345793 Kalina Jan UTIA-B Stochastická informatika Department of Stochastic Informatics SI SI Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2024/SI/kalina-0583644.pdf GA21-05325S GA ČR cav_un_auth*0409039 The primary aim of this work is to illustrate the importance of the choice of the appropriate methods for the statistical analysis of economic data. Typically, there exist several alternative versions of common statistical methods for every statistical modeling task and the most habitually used (“vanilla”) versions may yield rather misleading results in nonstandard situations. Linear regression is considered here as the most fundamental econometric model. First, the analysis of a world tourism dataset is presented, where the number of international arrivals is modeled for 140 countries of the world as a response of 14 pillars (indicators) of the Travel and Tourism Competitiveness Index. Heteroscedasticity is clearly recognized in the dataset. However, the Aitken estimator, which would be the standard remedy in such a situation, is revealed here to be very inappropriate. Regression quantiles represent a much more suitable solution here. The second illustration with artificial data reveals standard regression quantiles to be unsuitable for data contaminated by outlying values. Their recently proposed robust version turns out to be much more appropriate. Both illustrations reveal that choosing suitable methods represent an important (and often difficult) part of the analysis of economic data. cav_un_auth*0464145 INPROFORUM 2023: Challenges and Opportunities in the Digital World. International Scientific Conference /17./ 20231102 20231103 České Budějovice CZ BB 10000 10100 10103 2024 PR RVO:67985556 https://hdl.handle.net/11104/0351666 S 2023 SCOPUS PUBMED WOS cav_un_epca*0583640 Proceedings of the 17th International Scientific Conference INPROFORUM: Challenges and Opportunities in the Digital World University of South Bohemia in České Budějovice, Faculty of Economics 2023 České Budějovice 5 10 978-80-7694-053-6 2336-6788 Rolínek L. 340