Statistical Method Selection Matters: Vanilla Methods in Regression May Yield Misleading Results

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/67985807:_____/23:00583632

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Statistical Method Selection Matters: Vanilla Methods in Regression May Yield Misleading Results

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Statistical Method Selection Matters: Vanilla Methods in Regression May Yield Misleading Results

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA21-05325S" target="_blank" >GA21-05325S: Moderní neparametrické metody v ekonometrii</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

Počet stran výsledku

6

Strana od-do

5-10

Název nakladatele

University of South Bohemia in České Budějovice, Faculty of Economics

Místo vydání

České Budějovice

Místo konání akce

České Budějovice

Datum konání akce

2. 11. 2023

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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