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Original title:
Statistical Method Selection Matters: Vanilla Methods in Regression May Yield Misleading Results
Authors: Kalina, Jan
Document type: Papers
Conference/Event: INPROFORUM 2023: Challenges and Opportunities in the Digital World. International Scientific Conference /17./, České Budějovice (CZ), 20231102
Year: 2023
Language: eng
Abstract: 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 task and 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. Both illustrations reveal that choosing suitable methods represent an important (and often difficult) part of the analysis of economic data.
Keywords: assumptions; diagnostics; linear regression; non-standard situations; robustness
Project no.: GA21-05325S (CEP)
Funding provider: GA ČR
Host item entry: Proceedings of the 17th International Scientific Conference INPROFORUM: Challenges and Opportunities in the Digital World, ISBN 978-80-7694-053-6
Note: Související webová stránka: https://doi.org/10.32725/978-80-7694-053-6.63
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://inproforum.ef.jcu.cz/incfiles/inf-990000-0900_007.pdf
Original record: https://hdl.handle.net/11104/0351663
Permalink: http://www.nusl.cz/ntk/nusl-541555
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Authors: Kalina, Jan
Document type: Papers
Conference/Event: INPROFORUM 2023: Challenges and Opportunities in the Digital World. International Scientific Conference /17./, České Budějovice (CZ), 20231102
Year: 2023
Language: eng
Abstract: 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 task and 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. Both illustrations reveal that choosing suitable methods represent an important (and often difficult) part of the analysis of economic data.
Keywords: assumptions; diagnostics; linear regression; non-standard situations; robustness
Project no.: GA21-05325S (CEP)
Funding provider: GA ČR
Host item entry: Proceedings of the 17th International Scientific Conference INPROFORUM: Challenges and Opportunities in the Digital World, ISBN 978-80-7694-053-6
Note: Související webová stránka: https://doi.org/10.32725/978-80-7694-053-6.63
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://inproforum.ef.jcu.cz/incfiles/inf-990000-0900_007.pdf
Original record: https://hdl.handle.net/11104/0351663
Permalink: http://www.nusl.cz/ntk/nusl-541555
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Record created 2024-03-10, last modified 2024-04-15