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Modelling commuting time in the US: Bootstrapping techniques to avoid overfitting
Author(s) -
GimenezNadal José Ignacio,
Molina José Alberto,
Velilla Jorge
Publication year - 2019
Publication title -
papers in regional science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.937
H-Index - 64
eISSN - 1435-5957
pISSN - 1056-8190
DOI - 10.1111/pirs.12424
Subject(s) - overfitting , bootstrapping (finance) , computer science , econometrics , selection (genetic algorithm) , set (abstract data type) , model selection , econometric model , machine learning , feature selection , artificial intelligence , economics , artificial neural network , programming language
The research on commuting has emerged in recent decades, but the issue of whether the empirical techniques used are appropriate has not been analysed. Thus, results from prior research could be based on non‐accurate models, leading to misleading conclusions. We apply an algorithmic approach based on bootstrapping, variable selection, and mean absolute prediction errors, which is designed to avoid overfitting. Using the American Time Use Survey, we find that models with a reduced set of explanatory variables have similar accuracy to standard econometric models. Our results shed light on the importance of determining whether models can be overfitted.