Statistical Method Selection Matters: Vanilla Methods in Regression May Yield Misleading Results
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F23%3A00583644" target="_blank" >RIV/67985556:_____/23:00583644 - isvavai.cz</a>
Alternative codes found
RIV/67985807:_____/23:00583632
Result on the web
<a href="http://dx.doi.org/10.32725/978-80-7694-053-6.63" target="_blank" >http://dx.doi.org/10.32725/978-80-7694-053-6.63</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.32725/978-80-7694-053-6.63" target="_blank" >10.32725/978-80-7694-053-6.63</a>
Alternative languages
Result language
angličtina
Original language name
Statistical Method Selection Matters: Vanilla Methods in Regression May Yield Misleading Results
Original language description
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 tasknand 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. Bothnillustrations reveal that choosing suitable methods represent an important (and often difficult) part of the analysis of economic data.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
<a href="/en/project/GA21-05325S" target="_blank" >GA21-05325S: Modern nonparametric methods in econometrics</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 17th International Scientific Conference INPROFORUM: Challenges and Opportunities in the Digital World
ISBN
978-80-7694-053-6
ISSN
2336-6788
e-ISSN
2336-6788
Number of pages
6
Pages from-to
5-10
Publisher name
University of South Bohemia in České Budějovice, Faculty of Economics
Place of publication
České Budějovice
Event location
České Budějovice
Event date
Nov 2, 2023
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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