On the Effect of Human Resources on Tourist Infrastructure: New Ideas on Heteroscedastic Modeling Using Regression Quantiles

0535715 - ÚI 2021 RIV CZ eng C - Konferenční příspěvek (zahraniční konf.)
Kalina, Jan - Janáček, Patrik
On the Effect of Human Resources on Tourist Infrastructure: New Ideas on Heteroscedastic Modeling Using Regression Quantiles.
RELIK 2020. Conference Proceedings. Prague: Prague University of Economics and Business, 2020 - (Langhamrová, J.; Vrabcová, J.), s. 227-236. ISBN 978-80-245-2394-1.
[RELIK 2020: The International Conference /13./. Prague (CZ), 05.11.2020-06.11.2020]
Grant CEP: GA ČR(CZ) GA19-05704S
Grant ostatní: GA ČR(CZ) GA18-01137S
Institucionální podpora: RVO:67985807
Klíčová slova: tourism infrastructure * human resources * regression * robustness * regression quantiles
Obor OECD: Statistics and probability
https://relik.vse.cz/2020/download/pdf/294-Kalina-Jan-paper.pdf

Tourism represents an important sector of the economy in many countries around the world. In this work, we are interested in the effect of the Human Resources and Labor Market pillar of the Travel and Tourism Competitiveness Index on tourist service infrastructure across 141 countries of the world. A regression analysis requires to handle heteroscedasticity in these data, which is not an uncommon situation in various available human capital studies. Our first task is focused on testing significance of individual variables in the model. It is illustrated here that significance tests are influenced by heteroscedasticity, which remains true also for tests for regression quantiles or robust regression estimators, resistant to a possible contamination of data by outliers. Only if a suitable model is considered, which takes heteroscedasticity into account, the effect of the Human Resources and Labor Market pillar turns out to be significant. Further, we propose and present a new diagnostic tool denoted as aquintile plot, allowing to interpret immediately the heteroscedastic structure of the linear regression model for possibly contaminated data.
Trvalý link: http://hdl.handle.net/11104/0313660