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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F19%3A00505226" target="_blank" >RIV/67985556:_____/19:00505226 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/19:00501434

Výsledek na webu

<a href="https://www.sav.sk/index.php?lang=sk&doc=journal-list&part=article_response_page&journal_article_no=16099" target="_blank" >https://www.sav.sk/index.php?lang=sk&doc=journal-list&part=article_response_page&journal_article_no=16099</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 multicollinear 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 modeled 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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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 multicollinear 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 modeled 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA17-07384S" target="_blank" >GA17-07384S: Neparametrické (statistické) metody v moderní ekonometrii</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Ekonomický časopis

ISSN

0013-3035

e-ISSN

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Svazek periodika

67

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

SK - Slovenská republika

Počet stran výsledku

17

Strana od-do

69-85

Kód UT WoS článku

000457791100005

EID výsledku v databázi Scopus

2-s2.0-85068216962