Least Weighted Squares Quantiles Reveal How Competitiveness Contributes to Tourism Performance

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F22%3A00559056" target="_blank" >RIV/67985807:_____/22:00559056 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/22:10452425

Výsledek na webu

<a href="https://doi.org/10.32065/CJEF.2022.02.03" target="_blank" >https://doi.org/10.32065/CJEF.2022.02.03</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.32065/CJEF.2022.02.03" target="_blank" >10.32065/CJEF.2022.02.03</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Least Weighted Squares Quantiles Reveal How Competitiveness Contributes to Tourism Performance

Popis výsledku v původním jazyce

Standard regression quantiles, which are commonly used in heteroscedastic regression models, are highly vulnerable with respect to the presence of leverage points in the data. The aim of this paper is to propose a novel robust version of regression quantiles, which are based on the idea to assign weights to individual observations. The novel method denoted as least weighted squares quantiles (LWSQ) is applied to a world tourism dataset, 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 (TTCI). Here, the economic motivation is to investigate whether tourism competitiveness promotes tourism performance. The data analysis reveals the advantages of LWSQ. Particularly, LWSQ is able to clearly outperform standard regression quantiles in several artificially contaminated versions of the tourism dataset. From the economic point of view, the study determines countries which are not effective in transforming their competitiveness to higher levels of tourist arrivals.

Název v anglickém jazyce

Least Weighted Squares Quantiles Reveal How Competitiveness Contributes to Tourism Performance

Popis výsledku anglicky

Standard regression quantiles, which are commonly used in heteroscedastic regression models, are highly vulnerable with respect to the presence of leverage points in the data. The aim of this paper is to propose a novel robust version of regression quantiles, which are based on the idea to assign weights to individual observations. The novel method denoted as least weighted squares quantiles (LWSQ) is applied to a world tourism dataset, 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 (TTCI). Here, the economic motivation is to investigate whether tourism competitiveness promotes tourism performance. The data analysis reveals the advantages of LWSQ. Particularly, LWSQ is able to clearly outperform standard regression quantiles in several artificially contaminated versions of the tourism dataset. From the economic point of view, the study determines countries which are not effective in transforming their competitiveness to higher levels of tourist arrivals.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Finance a úvěr-Czech Journal of Economics and Finance

ISSN

0015-1920

e-ISSN

0015-1920

Svazek periodika

72

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

22

Strana od-do

150-171

Kód UT WoS článku

000810458700003

EID výsledku v databázi Scopus

2-s2.0-85135351736